Data Science and Machine Learning

Group Project BUY&GO

Tiago Rodrigues

10-04-2021

Table of Contents

• 1. Import Libraries
  
• 2. Data pre-processing and Understanding
  
  • 2.1 Collect and merge the data
    
  • 2.2 Data caracterization
    
  • 2.3 Explore and understand the data
    
    • 2.3.1 Statistical Exploration
      
    • 2.3.2 Demographics
      
• 3. Data transformation
  
  • 3.1 Data cleaning
    
    • 3.1.1 Outliers
      
    • 3.1.2 Misclassifications
      
    • 3.1.3 Missing values
      
  • 3.2 Data transformation
    
    • 3.2.1 Create new variables
      
    • 3.2.2 Drop non-relevant and redudant variables
      
    • 3.2.3 Binning
      
  • 3.3 Check for incoherencies
    
  • 3.4 Create dummy Variables
    
  • 3.5 Normalize data
    
  • 3.6 Power Transform
    
  • 3.7 Create flag variables for the transformed variables
    
  • 3.8 Data Frame demographics
    
• 4. Modelling
  
  • 4.1 Clustering via K-means
    
    • 4.1.1 Product usage segmentation
      
    • 4.1.2 key Insights
      
    • 4.2.1 Costumer value segmentation (Recency vs Mnt vs Freq)
      
    • 4.2.2 Key insights
      
    • 4.3.1 Costumer segmentation by campaign acceptance
      
    • 4.3.2 Key insights
      
  • 4.4 Insights of crossing the different segmentations
    
  • 4.5 Clustering with Gaussian mixture vs Agglomerative Clustering
    

1. Import Libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import MinMaxScaler
import math
from matplotlib.patches import Patch
from matplotlib.lines import Line2D
from scipy.stats import boxcox
from scipy.special import inv_boxcox
from sklearn.preprocessing import power_transform
import scipy as sp
from scipy import stats
from matplotlib import gridspec
from numpy import unique
from numpy import where

from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler
from kneed import KneeLocator, DataGenerator
from scipy.cluster import hierarchy

Funções auxiliares

def stats (lista):
    
    median = '{:.2f}'.format(lista.median())
    Q1 = '{:.2f}'.format(np.percentile(lista, 25))
    Q3 = '{:.2f}'.format(np.percentile(lista, 75))
    skew = '{:.2f}'.format(sp.stats.skew(lista) )
    return (median, Q1, Q3, skew)

def stats2 (lista):
    
    mean = '{:.2f}'.format(lista.mean())
    Q1 = '{:.2f}'.format(np.percentile(lista, 25))
    Q3 = '{:.2f}'.format(np.percentile(lista, 75))
    skew = '{:.2f}'.format(sp.stats.skew(lista) )
    return (mean, Q1, Q3, skew)

2. Data pre-processing and Understanding

2.1 Collect and merge the data

df1_original = pd.read_excel('demographic.xlsx')
df2_original = pd.read_csv('firmographic.csv')

df1 = df1_original
df1.set_index('Card_ID', inplace = True)
df1.drop_duplicates(inplace = True)
df1.head(n=5)

	Name	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Region	Country
Card_ID
10003075	Mr. Adam Glover	1981	Master	Together	90782.0	0	0	Cork	Ireland
10003076	Mr. Cameron McDonald	1993	PhD	Single	113023.0	0	0	Kinsale	Ireland
10003078	Mr. Keith Davidson	1982	PhD	Single	93571.0	0	1	Cork	Ireland
10003079	Mr. Alexander Gill	1978	PhD	Single	91852.0	0	1	Kinsale	Ireland
10003080	Mr. Neil Piper	1968	PhD	Married	22386.0	1	1	Cork	Ireland

df2 = df2_original
df2.set_index('Card_ID', inplace = True)
df2.drop_duplicates(inplace = True)
df2.head(n=5)

	Dt_Customer	Recency	MntGroceries	MntStationery	MntHouseKeeping	MntWellness_&_Beauty	MntElectronics_&_Supplies	MntLimitedEdition	NumDealsPurchases	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp1	AcceptedCmp2	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	Complain
Card_ID
10003075	11/3/2018	66	622.0	70.0	678.0	NaN	51.0	34.0	1	12	7	10	4	0	1	0	0	0	0
10003076	4/18/2019	6	1014.0	15.0	643.0	74.0	36.0	36.0	1	9	4	8	1	0	0	0	0	0	0
10003078	7/22/2018	10	639.0	88.0	185.0	64.0	53.0	160.0	3	13	5	13	5	0	0	0	0	0	0
10003079	2/3/2018	26	806.0	56.0	350.0	54.0	54.0	260.0	1	15	9	11	6	0	0	0	1	0	0
10003080	10/31/2018	65	32.0	1.0	29.0	2.0	0.0	20.0	5	9	3	3	9	1	0	0	0	0	0

df = df1.merge(df2, on = 'Card_ID' )
df.head(n=5)

	Name	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Region	Country	Dt_Customer	...	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp1	AcceptedCmp2	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	Complain
Card_ID
10003075	Mr. Adam Glover	1981	Master	Together	90782.0	0	0	Cork	Ireland	11/3/2018	...	12	7	10	4	0	1	0	0	0	0
10003076	Mr. Cameron McDonald	1993	PhD	Single	113023.0	0	0	Kinsale	Ireland	4/18/2019	...	9	4	8	1	0	0	0	0	0	0
10003078	Mr. Keith Davidson	1982	PhD	Single	93571.0	0	1	Cork	Ireland	7/22/2018	...	13	5	13	5	0	0	0	0	0	0
10003079	Mr. Alexander Gill	1978	PhD	Single	91852.0	0	1	Kinsale	Ireland	2/3/2018	...	15	9	11	6	0	0	0	1	0	0
10003080	Mr. Neil Piper	1968	PhD	Married	22386.0	1	1	Cork	Ireland	10/31/2018	...	9	3	3	9	1	0	0	0	0	0

5 rows × 28 columns

2.2 Data caracterization

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 5000 entries, 10003075 to 10013073
Data columns (total 28 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Name                       4999 non-null   object 
 1   Year_Birth                 5000 non-null   int64  
 2   Education                  5000 non-null   object 
 3   Marital_Status             5000 non-null   object 
 4   Income                     4948 non-null   float64
 5   Kidhome                    5000 non-null   int64  
 6   Teenhome                   5000 non-null   int64  
 7   Region                     5000 non-null   object 
 8   Country                    5000 non-null   object 
 9   Dt_Customer                5000 non-null   object 
 10  Recency                    5000 non-null   int64  
 11  MntGroceries               5000 non-null   float64
 12  MntStationery              5000 non-null   float64
 13  MntHouseKeeping            4907 non-null   float64
 14  MntWellness_&_Beauty       4935 non-null   float64
 15  MntElectronics_&_Supplies  5000 non-null   float64
 16  MntLimitedEdition          5000 non-null   float64
 17  NumDealsPurchases          5000 non-null   int64  
 18  NumWebPurchases            5000 non-null   int64  
 19  NumCatalogPurchases        5000 non-null   int64  
 20  NumStorePurchases          5000 non-null   int64  
 21  NumWebVisitsMonth          5000 non-null   int64  
 22  AcceptedCmp1               5000 non-null   int64  
 23  AcceptedCmp2               5000 non-null   int64  
 24  AcceptedCmp3               5000 non-null   int64  
 25  AcceptedCmp4               5000 non-null   int64  
 26  AcceptedCmp5               5000 non-null   int64  
 27  Complain                   5000 non-null   int64  
dtypes: float64(7), int64(15), object(6)
memory usage: 1.1+ MB

Data frame contains the following types of data:

• Categorical:
  • Nominal : 'Education', 'Marital_Status', 'Region', 'Country', 'Name '
  • Dummy : 'AcceptedCmp1' ; 'AcceptedCmp2' ; 'AcceptedCmp3' ; 'AcceptedCmp4' ; 'AcceptedCmp5' ; 'Complain'
• Numerical:
  • Interval : 'Custid', 'Year_Birth', 'Dt_Customer',
  • Ratio : 'Income' , 'Kidhome' , 'Teenhome' , 'Recency' , 'MntGroceries' , 'MntStationery' , 'MntHouseKeeping' , 'MntWellness_&Beauty', 'MntElectronics&_Supplies', 'MntLimitedEdition' , 'NumDealsPurchases' ,'NumWebPurchases', 'NumCatalogPurchases',n'NumStorePurchases' , 'NumWebVisitsMonth'

#Including only numeric columns in a DataFrame description.
df.describe(include=[np.number]).T

	count	mean	std	min	25%	50%	75%	max
Year_Birth	5000.0	1973.806600	11.870138	1946.0	1964.0	1975.0	1983.00	2002.00
Income	4948.0	69614.564198	28953.396355	2195.8	47075.5	69427.0	92119.75	187314.45
Kidhome	5000.0	0.440600	0.538265	0.0	0.0	0.0	1.00	2.00
Teenhome	5000.0	0.493600	0.546643	-1.0	0.0	0.0	1.00	2.00
Recency	5000.0	49.576400	29.000593	0.0	25.0	49.0	75.00	99.00
MntGroceries	5000.0	363.336740	400.096463	0.0	30.0	209.0	605.25	1799.00
MntStationery	5000.0	27.041220	40.069024	0.0	2.0	9.0	34.00	199.00
MntHouseKeeping	4907.0	185.105910	240.357283	0.0	18.0	73.0	256.50	1093.00
MntWellness_&_Beauty	4935.0	32.237285	47.312292	0.0	3.0	11.0	41.00	239.00
MntElectronics_&_Supplies	5000.0	35.049700	54.325949	0.0	3.0	11.0	42.00	315.00
MntLimitedEdition	5000.0	55.765500	69.240596	0.0	11.0	30.0	71.00	424.00
NumDealsPurchases	5000.0	2.258800	1.812313	0.0	1.0	2.0	3.00	15.00
NumWebPurchases	5000.0	10.244800	5.347990	1.0	8.0	10.0	12.00	231.00
NumCatalogPurchases	5000.0	4.546400	2.802395	0.0	2.0	4.0	6.00	13.00
NumStorePurchases	5000.0	5.730800	3.275191	0.0	3.0	5.0	8.00	13.00
NumWebVisitsMonth	5000.0	5.278600	3.148677	0.0	3.0	6.0	7.00	137.00
AcceptedCmp1	5000.0	0.080200	0.271630	0.0	0.0	0.0	0.00	1.00
AcceptedCmp2	5000.0	0.068600	0.252798	0.0	0.0	0.0	0.00	1.00
AcceptedCmp3	5000.0	0.077800	0.267883	0.0	0.0	0.0	0.00	1.00
AcceptedCmp4	5000.0	0.064200	0.245133	0.0	0.0	0.0	0.00	1.00
AcceptedCmp5	5000.0	0.011400	0.106171	0.0	0.0	0.0	0.00	1.00
Complain	5000.0	0.012400	0.110674	0.0	0.0	0.0	0.00	1.00

From the above table, the following conclusions are drawn:

• count- The income variable has 4948 valid values. We have a problem of missing values in here. Moreover, some customers did not bought anything from 'Housekeeping' label products, or/and 'Wellness_&_Beauty'.
• mean- In average, my customers have an income of 69614, and Groceries is in average the category where costumers spend more money.
• std- The standard deviation of Income is quite high, which indicates that the values are spread out over a wider range.
• max- Both the maximum number of visits per month and number of web purchases are quite high, which might be an outlier or error.

#Including only string columns in a DataFrame description
df.describe(include=[object]).T

	count	unique	top	freq
Name	4999	4999	Mr. Christopher Short	1
Education	5000	11	PhD	2662
Marital_Status	5000	5	Married	2108
Region	5000	3	Kinsale	1695
Country	5000	3	Ireland	4994
Dt_Customer	5000	700	6/24/2018	18

From the table above the following can be concluded:

• Name - There is a client that chose to remain anonymous, or failled to insert his/her Name
• Education - The 11 unique entries must be reviewed as this name appears to be too high.

2.3 Explore and understand the data

2.3.1 Statistical Exploration

kt = df.skew()
kt 

Year_Birth                   -0.104376
Income                        0.074944
Kidhome                       0.653025
Teenhome                      0.429628
Recency                      -0.008955
MntGroceries                  1.163000
MntStationery                 2.073507
MntHouseKeeping               1.662145
MntWellness_&_Beauty          2.081755
MntElectronics_&_Supplies     2.392493
MntLimitedEdition             2.313686
NumDealsPurchases             1.881128
NumWebPurchases              22.185112
NumCatalogPurchases           1.083225
NumStorePurchases             0.665959
NumWebVisitsMonth            14.689330
AcceptedCmp1                  3.092209
AcceptedCmp2                  3.414368
AcceptedCmp3                  3.153379
AcceptedCmp4                  3.557038
AcceptedCmp5                  9.207698
Complain                      8.815006
dtype: float64

#Function that list collumns with a kurt value higher then 3

kt = df.kurt()
kt 

Year_Birth                    -0.830261
Income                        -0.556396
Kidhome                       -0.752638
Teenhome                      -0.913354
Recency                       -1.201001
MntGroceries                   0.570844
MntStationery                  3.976999
MntHouseKeeping                2.004285
MntWellness_&_Beauty           4.042067
MntElectronics_&_Supplies      6.106466
MntLimitedEdition              6.498925
NumDealsPurchases              4.731187
NumWebPurchases              796.641075
NumCatalogPurchases            0.543071
NumStorePurchases             -0.652685
NumWebVisitsMonth            611.872912
AcceptedCmp1                   7.564783
AcceptedCmp2                   9.661771
AcceptedCmp3                   7.946978
AcceptedCmp4                  10.656782
AcceptedCmp5                  82.814821
Complain                      75.734630
dtype: float64

If the dummy variables are filtered from the above list, the following collumns might need a special attention , due to its high kurtosis value : 'MntStationery', 'MntWellness_&Beauty', 'MntElectronics&_Supplies', 'MntLimitedEdition', 'NumDealsPurchases', 'NumWebPurchases', 'NumWebVisitsMonth'

for index, val in kt.iteritems():
    
    if val > 3 :
        print(index)

MntStationery
MntWellness_&_Beauty
MntElectronics_&_Supplies
MntLimitedEdition
NumDealsPurchases
NumWebPurchases
NumWebVisitsMonth
AcceptedCmp1
AcceptedCmp2
AcceptedCmp3
AcceptedCmp4
AcceptedCmp5
Complain

df_corr = df.corr(method = 'spearman')
figure = plt.figure(figsize=(16,10))
sns.heatmap(df_corr, annot=True, fmt = '.1g')

<matplotlib.axes._subplots.AxesSubplot at 0x20159219848>

2.3.2 Demographics

fig, axes = plt.subplots(1,2, figsize = (15,6))

sns.histplot (ax = axes[0], data = df, x = 'MntStationery',kde=True,fill=False)
sns.histplot (ax = axes[1], data = df, x = 'MntWellness_&_Beauty',kde=True,fill=False)

mean_1 = round(df['MntStationery'].mean(),2)
mean_2 = round(df['MntWellness_&_Beauty'].mean(),2)

sdeviation_1 = round(df['MntStationery'].std(),2)
sdeviation_2 = round(df['MntWellness_&_Beauty'].std(),2)

median_1 = round(df['MntStationery'].median(),2)
median_2 = round(df['MntWellness_&_Beauty'].median(),2)

skew_1 = round(df['MntStationery'].skew(),2)
skew_2 = round(df['MntWellness_&_Beauty'].skew(),2)

string_1 = ' \u03BC = ' + str(mean_1) + "\n \u03C3 =" + str(sdeviation_1) + "\n median = " + str(median_1)
string_2 = ' \u03BC = ' + str(mean_2) + "\n \u03C3 =" + str(sdeviation_2) + "\n median = " + str(median_2)


props = dict(boxstyle='round', facecolor='white', alpha=0.5)   

axes[0].text(0.6, 0.8, string_1, transform=axes[0].transAxes, fontsize=14,
        verticalalignment='top' ,bbox=props)
axes[0].text(0.6, 0.6, "Skewness = " + str(skew_1), transform=axes[0].transAxes, fontsize=14,
       color= 'blue')

axes[1].text(0.6, 0.8, string_2, transform=axes[1].transAxes, fontsize=14,
        verticalalignment='top' ,bbox=props)
axes[1].text(0.6, 0.6, "Skewness = " + str(skew_2), transform=axes[1].transAxes, fontsize=14,
       color= 'blue')
plt.show()

fig, axes = plt.subplots(1,2, figsize = (15,6))

sns.histplot (ax = axes[0], data = df, x = 'MntElectronics_&_Supplies',kde=True,fill=False)
sns.histplot (ax = axes[1], data = df, x = 'MntLimitedEdition',kde=True,fill=False)

mean_1 = round(df['MntElectronics_&_Supplies'].mean(),2)
mean_2 = round(df['MntLimitedEdition'].mean(),2)

sdeviation_1 = round(df['MntElectronics_&_Supplies'].std(),2)
sdeviation_2 = round(df['MntLimitedEdition'].std(),2)

median_1 = round(df['MntElectronics_&_Supplies'].median(),2)
median_2 = round(df['MntLimitedEdition'].median(),2)

skew_1 = round(df['MntElectronics_&_Supplies'].skew(),2)
skew_2 = round(df['MntLimitedEdition'].skew(),2)

string_1 = ' \u03BC = ' + str(mean_1) + "\n \u03C3 =" + str(sdeviation_1) + "\n median = " + str(median_1)
string_2 = ' \u03BC = ' + str(mean_2) + "\n \u03C3 =" + str(sdeviation_2) + "\n median = " + str(median_2)


props = dict(boxstyle='round', facecolor='white', alpha=0.5)   

axes[0].text(0.6, 0.8, string_1, transform=axes[0].transAxes, fontsize=14,
        verticalalignment='top' ,bbox=props)
axes[0].text(0.6, 0.6, "Skewness = " + str(skew_1), transform=axes[0].transAxes, fontsize=14,
       color= 'blue')

axes[1].text(0.6, 0.8, string_2, transform=axes[1].transAxes, fontsize=14,
        verticalalignment='top' ,bbox=props)
axes[1].text(0.6, 0.6, "Skewness = " + str(skew_2), transform=axes[1].transAxes, fontsize=14,
       color= 'blue')

plt.show()

3. Data transformation

df_treated = df.copy()

3.1 Data cleaning

3.1.1 Outliers

fig, axes = plt.subplots(3,1, figsize = (15,10))
sns.boxplot(ax = axes[0], data = df_treated , x = 'NumDealsPurchases')
sns.boxplot(ax = axes[1], data = df_treated , x = 'NumWebPurchases')
sns.boxplot(ax = axes[2], data = df_treated , x = 'NumWebVisitsMonth')

<matplotlib.axes._subplots.AxesSubplot at 0x20154425c08>

df_treated  = df_treated .drop(df_treated [df_treated ['NumWebVisitsMonth'] > 40].index)
df_treated  = df_treated .drop(df_treated [df_treated ['NumWebPurchases'] > 50].index)
df_treated  = df_treated .drop(df_treated [df_treated ['NumDealsPurchases'] > 6].index)

3.1.2 Misclassifications

#Explore "Education" entries
df_treated ['Education'].value_counts()

PhD           2566
Graduation    1331
Master         818
2n Cycle        39
GRADUATION      22
PHD             14
MASTER           9
MATSER           4
graduation       3
phd              2
2N CYCLE         1
Name: Education, dtype: int64

df_treated ['Education'] = df_treated ['Education'].str.capitalize() 
df_treated ['Education'].value_counts()

Phd           2582
Graduation    1356
Master         827
2n cycle        40
Matser           4
Name: Education, dtype: int64

df_treated ['Education'] = df_treated ['Education'].replace('Matser','Master')
df_treated ['Education'].value_counts()

Phd           2582
Graduation    1356
Master         831
2n cycle        40
Name: Education, dtype: int64

# Exploring 'Marital_Status' entries
df_treated ['Marital_Status'].value_counts()

Married     2029
Together    1208
Single      1020
Divorced     412
Widow        140
Name: Marital_Status, dtype: int64

df_treated ['Marital_Status'] = df_treated ['Marital_Status'].replace('Together','Married')
df_treated ['Marital_Status'].value_counts()

Married     3237
Single      1020
Divorced     412
Widow        140
Name: Marital_Status, dtype: int64

# Exploring 'Region' entries
df_treated ['Region'].value_counts()

Kinsale      1630
Killarney    1615
Cork         1564
Name: Region, dtype: int64

# Exploring 'Country' entries
df_treated ['Country'].value_counts()

Ireland    4805
UK            2
France        2
Name: Country, dtype: int64

# Exploring 'Kid_home' entries
df_treated ['Kidhome'].value_counts()

0    2855
1    1849
2     105
Name: Kidhome, dtype: int64

# Exploring 'Teenhome' entries
df_treated ['Teenhome'].value_counts()

 0    2621
 1    2076
 2     107
-1       5
Name: Teenhome, dtype: int64

#See entries with teenhome = -1
df_treated .loc[df_treated ['Teenhome'] == -1]

	Name	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Region	Country	Dt_Customer	...	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp1	AcceptedCmp2	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	Complain
Card_ID
10003214	Mr. Jack Wilkins	1991	Graduation	Married	104547.0	0	-1	Cork	Ireland	4/18/2018	...	12	12	10	3	0	1	1	1	0	0
10003351	Mrs. Zoe Gray	1976	Phd	Married	109238.0	1	-1	Kinsale	Ireland	9/30/2017	...	10	6	10	6	1	0	1	0	0	0
10003500	Mr. Connor Taylor	1983	Graduation	Married	31286.0	1	-1	Killarney	Ireland	8/15/2017	...	9	3	3	9	0	0	0	0	0	0
10003712	Mr. Austin Bell	1967	Phd	Single	82877.0	0	-1	Cork	Ireland	11/16/2017	...	10	5	6	3	0	0	0	0	0	0
10003808	Mr. Jacob Mackay	1966	Phd	Married	83838.0	0	-1	Cork	Ireland	10/11/2017	...	12	7	10	5	0	0	0	0	0	0

5 rows × 28 columns

df_treated ['Teenhome'] = df_treated ['Teenhome'].replace(-1,df_treated ['Teenhome'].value_counts().idxmax())
df_treated ['Teenhome'].value_counts()

0    2626
1    2076
2     107
Name: Teenhome, dtype: int64

# Exploring 'Country' entries
df_treated ['Country'].value_counts()

Ireland    4805
UK            2
France        2
Name: Country, dtype: int64

dropped = (np.shape(df)[0] - np.shape(df_treated)[0])/np.shape(df)[0] *100

print("Total dropped rows:", str(dropped),"%")

Total dropped rows: 3.82 %

3.1.3 Missing values

df_treated .isna().sum()

Name                          1
Year_Birth                    0
Education                     0
Marital_Status                0
Income                       51
Kidhome                       0
Teenhome                      0
Region                        0
Country                       0
Dt_Customer                   0
Recency                       0
MntGroceries                  0
MntStationery                 0
MntHouseKeeping              87
MntWellness_&_Beauty         63
MntElectronics_&_Supplies     0
MntLimitedEdition             0
NumDealsPurchases             0
NumWebPurchases               0
NumCatalogPurchases           0
NumStorePurchases             0
NumWebVisitsMonth             0
AcceptedCmp1                  0
AcceptedCmp2                  0
AcceptedCmp3                  0
AcceptedCmp4                  0
AcceptedCmp5                  0
Complain                      0
dtype: int64

df_treated .loc[df_treated ['MntHouseKeeping'] == 0]

	Name	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Region	Country	Dt_Customer	...	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp1	AcceptedCmp2	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	Complain
Card_ID
10005718	Mrs. Sophie Black	1961	Graduation	Single	15264.0	0	0	Cork	Ireland	12/16/2018	...	7	2	2	6	0	0	0	0	0	0
10007820	Mr. William Reid	1962	Master	Widow	21757.0	1	1	Cork	Ireland	5/25/2019	...	6	2	3	2	0	0	0	0	0	0

2 rows × 28 columns

df_treated .loc[df_treated ['MntWellness_&_Beauty'] == 0]

	Name	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Region	Country	Dt_Customer	...	NumWebPurchases	NumCatalogPurchases	NumStorePurchases	NumWebVisitsMonth	AcceptedCmp1	AcceptedCmp2	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	Complain
Card_ID
10003101	Miss Karen Short	1956	Phd	Married	59583.00	1	1	Cork	Ireland	2/17/2018	...	8	2	3	7	0	0	0	0	0	0
10003105	Mrs. Jane Bower	1978	Phd	Married	30957.00	1	0	Killarney	Ireland	9/15/2018	...	7	2	2	8	0	0	0	0	0	0
10003106	Mr. Brian Walker	1970	Master	Married	52474.00	2	1	Kinsale	Ireland	3/20/2019	...	7	2	2	6	1	0	0	0	0	0
10003116	Mr. Dan Randall	1970	Master	Married	49108.00	1	1	Kinsale	Ireland	7/13/2018	...	7	3	3	5	0	0	0	0	0	0
10003125	Mr. Evan Knox	1958	Phd	Married	36908.00	0	1	Killarney	Ireland	5/4/2018	...	7	2	3	4	0	0	0	0	0	0
10003130	Mr. Adrian Hill	1996	Graduation	Single	25634.00	1	0	Kinsale	Ireland	4/17/2018	...	9	2	3	9	0	0	0	0	0	0
10003159	Mr. Jack Mathis	1977	Master	Single	50847.00	1	0	Killarney	Ireland	4/23/2018	...	9	3	2	8	1	0	0	0	0	0
10003168	Miss Dorothy Russell	1970	Phd	Married	90240.00	0	1	Cork	Ireland	6/12/2018	...	13	5	8	5	0	0	0	0	0	0
10003169	Mr. Owen Jackson	1991	Phd	Single	35844.00	1	0	Kinsale	Ireland	1/8/2019	...	7	2	3	8	1	0	0	0	0	0
10003171	Miss Amy King	1960	Phd	Married	55137.00	1	2	Cork	Ireland	5/4/2019	...	8	3	3	4	0	0	0	0	0	0
10003174	Mr. Simon Tucker	1957	Master	Married	97204.00	0	0	Killarney	Ireland	10/30/2018	...	13	7	8	4	0	1	1	1	0	0
10003175	Mr. Boris Rees	1951	Phd	Married	47267.00	0	1	Kinsale	Ireland	3/2/2019	...	7	2	3	5	0	0	0	0	0	0
10003182	Mr. Stephen Scott	1968	Phd	Divorced	10000.00	0	0	Killarney	Ireland	11/25/2018	...	8	2	3	7	0	0	0	0	0	0
10003188	Mr. Jason MacLeod	1976	Master	Single	56646.00	1	0	Kinsale	Ireland	5/12/2019	...	7	2	2	6	0	0	0	0	0	0
10003191	Miss Anna Fraser	1964	Master	Married	66027.00	1	1	Killarney	Ireland	6/5/2018	...	7	2	2	5	0	0	0	0	0	0
10003211	Mr. Tim Hart	1957	Graduation	Single	100096.00	0	1	Killarney	Ireland	1/24/2018	...	11	6	8	3	0	0	0	0	0	0
10003219	Mr. Gavin Dyer	1952	Phd	Married	65582.00	0	1	Cork	Ireland	1/21/2019	...	8	2	3	7	0	0	0	0	0	0
10003239	Mr. Oliver Clarkson	1987	Phd	Married	43284.00	1	0	Kinsale	Ireland	10/11/2018	...	8	3	3	7	0	0	0	0	0	0
10003241	Mr. Colin Hudson	1978	Phd	Married	61649.00	1	1	Kinsale	Ireland	1/3/2018	...	8	3	2	7	0	0	0	0	0	0
10003243	Mr. Joseph Avery	1976	Phd	Single	65043.00	1	1	Cork	Ireland	10/14/2018	...	7	2	3	6	0	0	0	0	0	0
10003253	Mr. Max Hart	1972	Phd	Married	54582.00	0	1	Killarney	Ireland	4/20/2019	...	11	4	4	7	0	1	0	0	0	0
10003264	Mr. Harry Lee	1960	Phd	Single	80250.00	0	2	Cork	Ireland	8/19/2017	...	8	4	9	8	0	0	0	0	0	0
10003270	Mr. Paul Peters	1975	Phd	Married	81488.00	1	0	Kinsale	Ireland	11/26/2017	...	8	4	8	8	0	0	0	0	0	0
10003306	Mr. Sean Oliver	1965	Phd	Married	39912.00	1	1	Killarney	Ireland	1/13/2019	...	7	2	3	7	0	0	0	0	0	0
10003310	Mr. Warren Ross	1979	Phd	Married	67136.00	0	1	Killarney	Ireland	8/14/2018	...	13	3	7	7	0	1	0	0	0	0
10003321	Miss Theresa Carr	1969	Phd	Married	69233.00	0	1	Cork	Ireland	1/23/2019	...	9	3	3	6	0	0	0	0	0	0
10003329	Mr. Robert Lawrence	1972	Phd	Married	113663.00	2	1	Kinsale	Ireland	9/24/2018	...	17	8	11	5	1	0	0	0	0	0
10003339	Miss Natalie Smith	1969	Phd	Single	61476.00	1	1	Cork	Ireland	5/27/2018	...	7	2	2	6	0	0	0	0	0	0
10003345	Mr. Christopher Ince	1959	Phd	Married	41124.00	1	1	Kinsale	Ireland	5/27/2019	...	7	3	2	5	1	0	0	0	0	0
10003357	Mr. Cameron Rutherford	1971	Phd	Single	80710.00	0	1	Cork	Ireland	6/25/2019	...	11	3	3	7	0	0	0	0	0	0
10003371	Mr. Christian McLean	1969	Phd	Single	50933.00	1	1	Kinsale	Ireland	12/8/2017	...	7	2	2	7	0	0	0	0	0	0
10003375	Mrs. Claire Vance	1956	Phd	Married	41980.00	0	0	Killarney	Ireland	11/3/2017	...	8	5	4	4	0	0	0	0	0	0
10003403	Mr. Jason Johnston	1968	Phd	Married	97804.00	0	0	Cork	Ireland	2/2/2019	...	17	5	5	5	0	0	0	0	0	0
10003419	Mr. Alexander Duncan	1981	Graduation	Single	87806.00	0	1	Kinsale	Ireland	12/2/2017	...	9	5	4	7	0	1	1	0	1	0
10003431	Miss Amanda Fraser	1968	Phd	Married	56250.00	0	1	Killarney	Ireland	3/13/2019	...	7	3	3	6	0	0	0	0	0	0
10003450	Miss Dorothy Hodges	1976	Graduation	Single	59518.00	1	0	Killarney	Ireland	6/14/2019	...	7	2	3	3	0	0	0	0	0	0
10003453	Mrs. Ella Robertson	1981	Phd	Married	42569.00	1	0	Cork	Ireland	1/9/2018	...	8	2	3	8	0	0	0	0	0	0
10003478	Mr. Leonard Paige	1970	Phd	Married	82915.00	0	2	Kinsale	Ireland	3/23/2019	...	12	4	10	4	0	0	0	0	0	0
10003495	Mr. Joe Morrison	1970	Phd	Single	72464.00	1	2	Killarney	Ireland	6/9/2019	...	8	2	4	6	0	0	0	0	0	0
10003503	Mr. Luke Gray	1971	Phd	Married	80227.00	0	1	Killarney	Ireland	12/11/2018	...	17	6	6	7	0	1	0	0	0	0
10003507	Mrs. Lily Henderson	1988	Phd	Married	110837.00	0	0	Kinsale	Ireland	8/19/2017	...	12	13	8	8	0	1	1	1	1	0
10003529	Mrs. Kylie Piper	1957	Master	Married	88081.00	0	1	Killarney	Ireland	12/19/2018	...	11	11	6	3	0	0	0	0	0	0
10003547	Miss Amy Wallace	1954	Phd	Married	70471.00	1	1	Cork	Ireland	3/25/2019	...	8	3	4	5	0	0	0	0	0	0
10003555	Mr. Christopher Nolan	1974	Phd	Married	47816.00	1	0	Kinsale	Ireland	10/30/2018	...	9	2	4	7	0	0	0	0	0	0
10003559	Mr. Christian Knox	1993	Master	Single	19842.00	1	0	Kinsale	Ireland	6/7/2019	...	7	2	3	7	0	0	0	0	0	0
10003579	Mr. Dan Graham	1965	Phd	Married	58873.00	1	1	Killarney	Ireland	1/20/2019	...	7	3	2	4	0	0	0	0	0	0
10003580	Mr. Ryan Grant	1965	Master	Married	82436.00	0	1	Kinsale	Ireland	3/17/2019	...	9	6	8	3	1	0	0	0	0	0
10003590	Mr. Nathan Kerr	1975	Phd	Married	64549.00	1	2	Killarney	Ireland	3/27/2019	...	6	2	3	6	0	0	0	0	0	0
10003604	Mr. Dylan Watson	1953	Phd	Married	50795.00	1	1	Cork	Ireland	10/16/2017	...	9	4	2	7	1	0	0	0	0	0
10003628	Mr. Brian Hodges	1978	Master	Married	64090.00	1	1	Cork	Ireland	10/10/2018	...	9	2	3	8	0	0	0	0	0	0
10003631	Mr. Gordon Davidson	1979	Phd	Married	44729.00	1	1	Killarney	Ireland	1/17/2018	...	8	2	4	7	0	0	0	0	0	0
10003634	Mrs. Theresa Lambert	1982	Phd	Single	80917.00	0	2	Kinsale	Ireland	4/7/2018	...	13	9	12	5	0	0	0	0	0	0
10003640	Mr. Liam Tucker	1962	Master	Single	50659.00	1	1	Cork	Ireland	8/26/2018	...	7	3	3	4	0	0	0	0	0	0
10003658	Miss Vanessa Hughes	1955	Phd	Married	134689.00	0	0	Cork	Ireland	9/21/2018	...	10	10	5	1	0	0	1	1	0	0
10003661	Mrs. Diane Gibson	1978	Master	Married	59066.00	1	1	Killarney	Ireland	6/24/2018	...	7	2	3	6	0	0	0	0	0	0
10003667	Mr. Sean Walsh	1977	Phd	Married	33795.00	1	1	Kinsale	Ireland	1/14/2018	...	7	2	3	7	0	0	0	0	0	0
10003674	Mr. Max Ogden	1978	Phd	Married	159607.05	1	1	Killarney	Ireland	12/25/2018	...	28	0	0	1	0	0	0	0	0	0
10003718	Mr. Piers Vance	1991	Phd	Married	47569.00	1	0	Killarney	Ireland	11/11/2018	...	7	2	3	6	0	0	0	0	0	0
10003722	Mr. Andrew Rees	1975	Phd	Married	53792.00	0	1	Killarney	Ireland	10/14/2018	...	10	3	5	7	0	0	0	0	0	0
10003726	Mr. Nathan Payne	1976	Master	Married	26717.00	1	0	Killarney	Ireland	2/1/2018	...	7	2	2	8	0	0	0	0	0	0
10003732	Mr. Sam Oliver	1964	Phd	Married	151364.85	1	1	Cork	Ireland	2/26/2018	...	1	0	1	1	0	0	0	0	0	0
10003782	Mr. Luke Ferguson	1977	Phd	Single	57480.00	1	0	Cork	Ireland	5/25/2019	...	8	2	3	6	0	0	0	0	0	0
10003794	Miss Karen North	1990	Master	Married	20095.00	1	0	Kinsale	Ireland	5/11/2018	...	9	3	3	5	0	0	0	0	0	0
10003796	Mr. Owen Smith	1967	Phd	Married	58792.00	1	1	Cork	Ireland	12/1/2018	...	8	2	3	6	0	0	0	0	0	0
10003797	Mr. Gavin Greene	1986	Phd	Single	38196.00	1	0	Cork	Ireland	8/22/2018	...	8	4	2	7	1	0	0	0	0	0
10003802	Mr. James Allan	1977	Phd	Single	68517.00	1	1	Kinsale	Ireland	8/10/2018	...	13	3	6	6	0	0	0	0	0	0
10003807	Mr. Victor Oliver	1955	Phd	Widow	77289.00	0	1	Kinsale	Ireland	5/12/2019	...	8	2	4	5	0	0	0	0	0	0
10003829	Mr. Jake Reid	1953	Phd	Married	65706.00	0	1	Killarney	Ireland	9/15/2018	...	9	4	7	4	0	0	0	0	0	0
10003838	Mrs. Gabrielle Powell	1966	Phd	Married	63067.00	0	1	Cork	Ireland	8/7/2018	...	10	3	5	5	0	0	0	0	0	0
10003844	Mr. Benjamin Newman	1959	Phd	Married	67547.00	0	1	Cork	Ireland	1/28/2019	...	7	2	3	5	0	0	0	0	0	0
10003845	Mr. Keith Welch	1958	Graduation	Married	47221.00	1	2	Cork	Ireland	3/4/2018	...	8	3	3	8	0	0	0	0	0	0
10003858	Mr. Adrian McGrath	1986	Graduation	Married	37988.00	1	0	Cork	Ireland	3/19/2019	...	8	3	2	6	0	0	0	0	0	0
10003867	Mr. Gavin Wright	1971	Phd	Married	69058.00	0	1	Kinsale	Ireland	11/21/2017	...	12	4	5	7	0	0	0	0	0	0
10003875	Mr. Stephen Hudson	1983	Master	Single	59669.00	0	1	Killarney	Ireland	12/2/2018	...	7	2	2	5	0	0	0	0	0	0
10003886	Mrs. Elizabeth Sharp	1971	Graduation	Single	38013.00	1	1	Kinsale	Ireland	4/14/2018	...	8	2	4	5	0	0	0	0	0	0
10003887	Mr. Isaac Newman	1978	Phd	Married	45639.00	1	1	Kinsale	Ireland	12/27/2017	...	7	2	2	7	0	0	0	0	0	0
10003897	Miss Grace Lambert	1976	Graduation	Single	NaN	1	0	Killarney	Ireland	5/7/2019	...	7	2	3	8	0	0	0	0	0	0
10003928	Mr. Steven McDonald	1973	Phd	Single	91928.00	1	0	Kinsale	Ireland	11/13/2018	...	14	6	6	4	0	1	1	0	0	0
10003959	Mr. Nicholas North	1965	Phd	Married	98748.00	0	1	Kinsale	Ireland	2/22/2018	...	17	6	7	6	1	0	1	1	0	0
10003979	Mr. Owen Allan	1977	Phd	Married	74022.00	2	1	Kinsale	Ireland	9/28/2017	...	17	3	6	9	0	0	0	0	0	0
10004001	Mr. Max Reid	1976	Phd	Married	53655.00	1	0	Kinsale	Ireland	3/10/2019	...	8	2	3	5	0	0	0	0	0	0
10004008	Mr. Christian White	1973	Phd	Married	44679.00	1	0	Kinsale	Ireland	2/28/2018	...	7	2	3	8	0	0	0	0	0	0
10004023	Miss Megan Forsyth	1960	Graduation	Married	88386.00	0	1	Cork	Ireland	5/14/2019	...	16	4	8	5	0	1	0	0	1	0
10004053	Miss Chloe Mitchell	1974	Phd	Single	7151.90	1	1	Kinsale	Ireland	5/28/2019	...	1	0	0	16	0	0	0	0	0	1
10004067	Mr. Joe Graham	1965	Phd	Single	62790.00	1	1	Kinsale	Ireland	3/13/2019	...	7	2	3	4	0	0	0	0	0	0
10004076	Mrs. Virginia Hardacre	1956	Phd	Married	77811.00	0	1	Cork	France	7/4/2018	...	17	9	4	8	1	0	0	0	0	0
10004082	Mr. Piers Powell	1983	Phd	Married	66241.00	0	1	Cork	Ireland	11/5/2018	...	13	3	7	7	0	0	0	0	0	0
10004095	Mr. Dominic Rees	1978	Graduation	Single	20492.00	1	0	Killarney	Ireland	3/1/2019	...	8	2	3	7	0	0	0	0	0	0
10004112	Mr. Nicholas Gill	1973	Phd	Married	30763.00	2	0	Killarney	Ireland	1/17/2018	...	8	2	3	9	0	0	0	0	0	0
10004115	Mr. Jacob Robertson	1977	Phd	Married	44410.00	1	0	Killarney	Ireland	1/13/2019	...	7	2	2	8	0	0	0	0	0	0
10004119	Miss Leah Watson	1972	Phd	Married	36513.00	1	0	Killarney	Ireland	1/24/2019	...	7	2	2	8	0	0	0	0	0	0
10004120	Mr. Brandon Burgess	1970	Phd	Married	38026.00	2	1	Cork	Ireland	6/15/2018	...	7	3	2	6	0	0	0	0	0	0
10004132	Mrs. Joan McDonald	1973	Phd	Married	42897.00	0	1	Killarney	Ireland	8/2/2018	...	7	2	3	4	0	0	0	0	0	0
10004150	Mr. Alexander Kelly	1974	Phd	Married	37185.00	1	0	Cork	Ireland	7/6/2018	...	7	3	2	6	0	0	0	0	0	0
10004176	Mr. Anthony Bower	1967	Phd	Married	60468.00	1	1	Kinsale	Ireland	9/7/2018	...	8	3	2	5	1	0	0	0	0	0
10004199	Mr. Colin Black	1981	Phd	Married	60247.00	0	2	Kinsale	Ireland	8/6/2018	...	7	2	2	6	0	0	0	0	0	0
10004214	Miss Abigail Jones	1974	Phd	Married	62599.00	0	1	Killarney	Ireland	4/10/2019	...	7	2	4	4	0	0	0	0	0	0
10004226	Mr. Frank Rampling	1993	Graduation	Married	49299.00	1	0	Killarney	Ireland	12/29/2018	...	7	2	3	7	0	0	0	0	0	0
10004254	Mrs. Joan Wilkins	1983	Phd	Divorced	37920.00	1	0	Cork	Ireland	7/8/2018	...	8	2	3	8	0	0	0	0	0	0
10004263	Mr. Jason Ogden	1959	Master	Widow	55873.00	1	1	Cork	Ireland	9/28/2017	...	7	2	3	7	0	0	0	0	0	0
10004267	Miss Lisa Martin	1990	Graduation	Married	47003.00	1	0	Killarney	Ireland	1/10/2019	...	7	2	2	7	0	0	0	0	0	0
10004281	Mrs. Deirdre Peake	1968	Phd	Single	49839.00	0	1	Killarney	Ireland	8/11/2017	...	12	4	9	7	0	0	0	0	0	0
10004285	Mr. Dylan Churchill	1975	Phd	Single	56537.00	1	0	Kinsale	Ireland	12/12/2018	...	7	2	3	8	0	0	0	0	0	0
10004297	Mr. Paul Rutherford	1969	Phd	Married	58030.00	1	1	Killarney	Ireland	4/13/2018	...	10	2	4	8	0	0	0	0	0	0
10004298	Mr. Julian Clark	1974	Phd	Married	81563.00	0	1	Killarney	Ireland	11/4/2018	...	11	4	8	8	1	0	0	0	0	1
10004328	Mr. Michael Taylor	1979	Master	Married	30589.00	1	0	Cork	Ireland	9/8/2017	...	7	2	2	8	0	0	0	0	0	0
10004341	Mr. Nathan MacLeod	1992	Phd	Single	81286.00	0	0	Cork	Ireland	3/1/2019	...	16	4	11	6	0	0	0	0	0	0
10004361	Mr. Gordon Morrison	1991	Master	Married	30617.00	1	0	Cork	Ireland	8/20/2017	...	7	2	2	9	0	0	0	0	0	0
10004368	Mr. Christopher MacLeod	1986	Phd	Married	34382.00	1	0	Cork	Ireland	4/14/2018	...	7	2	3	7	0	0	0	0	0	0
10004422	Mr. Joseph Bower	1986	Phd	Married	23906.00	1	0	Kinsale	Ireland	3/29/2019	...	9	2	3	8	0	0	0	0	0	0
10004430	Mrs. Penelope Ince	1978	Phd	Married	52506.00	1	1	Cork	Ireland	4/9/2018	...	6	3	2	6	0	0	0	0	0	0
10004434	Mrs. Amy Hill	1955	Phd	Single	60351.00	0	1	Kinsale	Ireland	6/24/2018	...	10	3	4	6	0	0	0	0	0	0
10004450	Mr. Trevor Quinn	1990	Phd	Married	45548.00	1	0	Kinsale	Ireland	10/31/2018	...	8	3	3	6	0	0	0	0	0	0
10004468	Mr. Joseph Hemmings	1989	Phd	Married	NaN	1	0	Killarney	Ireland	5/25/2018	...	9	2	3	8	0	0	0	0	0	0
10004469	Mr. Evan Arnold	1983	Phd	Married	67713.00	1	1	Cork	Ireland	9/11/2018	...	8	3	3	6	0	0	0	0	0	0
10004484	Mr. Carl Brown	1978	Graduation	Single	49798.00	0	0	Kinsale	Ireland	2/19/2018	...	6	2	3	4	0	0	0	0	0	0
10004503	Miss Michelle Hardacre	1977	Phd	Married	58325.00	1	1	Cork	Ireland	6/10/2018	...	11	3	5	7	0	0	0	0	0	0
10004526	Miss Heather Abraham	1953	Phd	Married	47120.00	1	1	Kinsale	Ireland	6/30/2019	...	8	2	3	5	0	0	0	0	0	0
10004554	Mrs. Caroline Lambert	1976	Phd	Single	32851.00	1	1	Killarney	Ireland	1/29/2019	...	8	2	4	6	0	0	0	0	0	0
10004587	Miss Diana Hart	1979	Phd	Married	45910.00	1	0	Cork	Ireland	6/29/2019	...	8	3	3	7	0	0	0	0	0	0
10004593	Mr. William Turner	1981	Phd	Married	78627.00	0	1	Kinsale	Ireland	2/13/2019	...	14	5	3	7	1	0	0	0	0	0
10004595	Mr. Christopher Lee	1997	Graduation	Single	10886.00	1	0	Killarney	Ireland	3/20/2018	...	7	3	2	7	0	0	0	0	0	0
10004601	Miss Jane Ferguson	1985	Graduation	Married	77635.00	0	1	Killarney	Ireland	8/15/2017	...	12	4	12	9	0	0	0	0	0	0
10004602	Mr. Warren Ferguson	1976	Phd	Married	41971.00	0	1	Cork	Ireland	12/15/2018	...	7	2	2	7	0	0	0	0	0	0
10004610	Mr. Lucas Jackson	1992	Graduation	Married	24064.00	1	0	Killarney	Ireland	4/4/2018	...	8	2	3	8	0	0	0	0	0	0
10004612	Mr. Anthony Anderson	1973	Graduation	Married	78280.00	0	1	Kinsale	Ireland	4/1/2018	...	17	7	9	6	0	0	0	0	0	0
10004625	Miss Andrea Murray	1972	Phd	Married	42167.00	1	0	Cork	Ireland	5/21/2018	...	9	2	4	5	0	0	0	0	0	0
10004628	Mr. Joe Lawrence	1984	Graduation	Married	58610.00	0	1	Kinsale	Ireland	2/19/2019	...	7	2	3	7	0	0	0	0	0	0
10004645	Mr. Isaac Sharp	1980	Phd	Divorced	44677.00	1	1	Kinsale	Ireland	8/14/2018	...	9	3	3	8	0	0	0	0	0	0
10004662	Mr. Boris Forsyth	1958	Phd	Married	79855.00	0	1	Killarney	Ireland	11/3/2018	...	8	3	3	3	0	0	0	0	0	0
10004668	Mrs. Carolyn White	1977	Phd	Married	65992.00	1	0	Cork	Ireland	4/26/2018	...	12	3	5	7	0	0	0	0	0	0
10004680	Mr. Dylan Paterson	1960	Master	Married	70872.00	0	1	Cork	Ireland	6/10/2019	...	9	3	4	4	0	0	0	0	0	0
10004712	Mr. Piers Blake	1958	Graduation	Widow	49658.00	1	1	Cork	Ireland	4/28/2018	...	7	3	2	7	1	0	0	0	0	0
10004724	Mrs. Heather Underwood	1987	Phd	Married	29320.00	1	0	Cork	Ireland	4/8/2018	...	7	2	2	7	0	0	0	0	0	0
10004733	Mr. Christopher Knox	1966	Phd	Divorced	52936.00	0	1	Killarney	Ireland	8/14/2018	...	10	3	4	7	0	0	0	0	0	0
10004753	Mrs. Jane Johnston	1974	Phd	Married	58453.00	0	1	Killarney	Ireland	6/19/2019	...	8	2	3	6	0	0	0	0	0	0
10004774	Mr. Oliver Forsyth	1966	Phd	Single	87253.00	0	1	Killarney	Ireland	9/4/2018	...	8	10	4	7	0	0	0	0	0	0
10004779	Mr. Max McGrath	1958	Master	Single	59581.00	0	1	Kinsale	Ireland	9/9/2018	...	8	2	4	5	0	0	0	0	0	0
10004799	Mr. Steven Johnston	1976	Phd	Single	28289.00	1	0	Kinsale	Ireland	1/8/2018	...	10	4	2	9	1	0	0	0	0	0
10004802	Mr. Jason Dickens	1961	Phd	Married	90048.00	0	1	Cork	Ireland	12/15/2018	...	13	5	12	4	0	0	0	0	0	0
10004812	Mr. John Sharp	1975	Phd	Married	65855.00	1	0	Kinsale	Ireland	11/23/2018	...	6	2	3	5	0	0	0	0	0	0
10004819	Miss Julia James	1953	Phd	Married	69556.00	0	1	Kinsale	Ireland	10/19/2017	...	13	5	5	8	0	1	0	0	0	0
10004834	Mrs. Molly Bond	1989	Graduation	Single	20688.00	1	0	Cork	Ireland	10/26/2017	...	7	2	2	7	1	0	0	0	0	0
10004873	Mr. Thomas Simpson	1964	Master	Single	57377.00	1	1	Cork	Ireland	10/3/2018	...	7	4	2	4	1	0	0	0	0	0
10004887	Mr. John Coleman	1975	Phd	Married	58655.00	1	0	Killarney	Ireland	8/22/2018	...	9	3	4	4	0	0	0	0	0	0
10004902	Mrs. Anne Harris	1972	Phd	Married	49882.00	1	0	Kinsale	Ireland	12/6/2017	...	6	2	3	7	0	0	0	0	0	0
10004913	Miss Molly Allan	1957	Phd	Single	58665.00	1	1	Kinsale	Ireland	12/14/2018	...	10	3	4	7	0	0	0	0	0	0
10004925	Mr. Harry Piper	1969	Phd	Married	82661.00	0	1	Killarney	Ireland	2/1/2018	...	12	6	8	5	0	1	0	1	1	0
10004944	Mr. Benjamin Davies	1967	Phd	Married	36317.00	1	1	Killarney	Ireland	4/21/2018	...	9	2	3	9	0	0	0	0	0	0
10004948	Mr. Steven Peake	1974	Phd	Married	33028.00	1	0	Kinsale	Ireland	3/25/2019	...	7	2	3	4	0	0	0	0	0	0
10004954	Mr. Victor Hill	1985	Master	Single	51697.00	1	0	Killarney	Ireland	2/13/2019	...	7	2	2	7	0	0	0	0	0	0
10004955	Mr. Sebastian Metcalfe	1969	Phd	Married	72335.00	2	1	Cork	Ireland	8/14/2018	...	7	2	3	5	0	0	0	0	0	0
10004965	Mrs. Joanne Randall	1987	Phd	Married	46367.00	1	0	Cork	Ireland	11/13/2018	...	7	2	2	6	0	0	0	0	0	0
10004979	Miss Chloe Dowd	1974	Phd	Married	40635.00	1	0	Cork	Ireland	1/23/2019	...	7	3	3	6	0	0	0	0	0	0
10004996	Mr. Gordon Edmunds	1988	Phd	Married	44615.00	1	0	Cork	Ireland	8/8/2018	...	8	2	3	7	0	0	0	0	0	0
10005012	Mr. Joshua Mackenzie	1965	Phd	Divorced	69167.00	0	1	Cork	Ireland	2/25/2018	...	14	4	8	8	0	0	0	0	0	0
10005025	Mr. Liam Morrison	1973	Phd	Married	78282.00	0	1	Cork	Ireland	5/12/2019	...	7	2	3	4	0	0	0	0	0	0
10005037	Mr. Dominic Parsons	1968	Phd	Married	62342.00	0	1	Kinsale	Ireland	11/17/2017	...	10	6	7	9	0	0	0	0	0	0
10005042	Miss Alexandra Mackenzie	1975	Phd	Married	63588.00	0	1	Killarney	Ireland	7/6/2018	...	7	2	2	6	0	0	0	0	0	0
10005065	Mrs. Heather Welch	1977	Graduation	Married	62900.00	0	1	Killarney	Ireland	5/22/2019	...	7	2	3	3	0	0	0	0	0	0
10005083	Mr. Lucas Alsop	1972	Phd	Married	46636.00	1	0	Kinsale	Ireland	10/29/2017	...	7	3	2	8	1	0	0	0	0	0
10005113	Mrs. Rose Graham	1985	Phd	Married	28470.00	1	0	Killarney	Ireland	1/1/2018	...	8	2	3	7	1	0	0	0	0	0
10005118	Miss Sally Metcalfe	1976	Phd	Married	40108.00	0	1	Cork	Ireland	2/22/2018	...	7	2	3	7	0	0	0	0	0	0
10005122	Mr. Jacob Thomson	1983	Phd	Married	117462.00	1	0	Cork	Ireland	8/19/2017	...	12	7	8	9	0	1	1	1	0	0
10005129	Mr. Dan Hunter	1960	Phd	Divorced	72972.00	0	1	Cork	Ireland	12/11/2017	...	13	7	10	7	0	0	0	0	0	0
10005166	Mrs. Audrey Walsh	1975	Phd	Married	67976.00	0	0	Kinsale	Ireland	2/28/2019	...	7	2	4	3	0	0	0	0	0	0
10005175	Mr. Ryan Randall	1982	Phd	Married	34006.00	1	0	Killarney	Ireland	7/24/2018	...	7	2	2	8	1	0	0	0	0	0
10005182	Miss Jessica Gray	1974	Graduation	Married	60388.00	0	1	Kinsale	Ireland	6/8/2019	...	7	2	3	6	0	0	0	0	0	0
10005183	Mrs. Emily Lyman	1968	Phd	Single	59409.00	1	1	Kinsale	Ireland	1/1/2018	...	8	2	3	8	1	0	0	0	0	0
10005184	Mr. Dylan MacDonald	1974	Graduation	Married	46625.00	1	1	Killarney	Ireland	4/5/2018	...	7	2	2	9	0	0	0	0	0	0
10005208	Mr. Leonard King	1986	Phd	Single	40613.00	1	0	Killarney	Ireland	1/16/2018	...	8	2	3	8	0	0	0	0	0	0
10005229	Mr. Liam Abraham	1968	Graduation	Married	21900.00	1	1	Cork	Ireland	6/9/2018	...	7	2	2	9	1	0	0	0	0	0
10005239	Mr. Robert Metcalfe	1977	Phd	Divorced	45621.00	1	0	Killarney	Ireland	4/16/2019	...	7	2	2	7	0	0	0	0	0	0
10005241	Miss Claire McDonald	1976	Phd	Married	38662.00	1	0	Cork	Ireland	11/1/2018	...	7	2	2	8	0	0	0	0	0	0
10005253	Mr. Stewart Hardacre	1978	Graduation	Married	52171.00	1	1	Killarney	Ireland	5/21/2019	...	7	3	3	4	0	0	0	0	0	0
10005254	Mrs. Caroline James	1992	Graduation	Married	36028.00	1	0	Cork	Ireland	9/26/2018	...	8	2	3	6	0	0	0	0	0	0
10005260	Mrs. Audrey Glover	1990	Phd	Single	44244.00	1	0	Cork	Ireland	4/7/2019	...	9	2	3	5	0	0	0	0	0	0
10005267	Miss Madeleine Paterson	1949	Phd	Widow	79980.00	0	0	Cork	Ireland	4/2/2019	...	11	3	8	4	0	0	0	0	0	0
10005268	Mr. Julian Brown	1957	Phd	Single	41049.00	0	1	Kinsale	Ireland	11/3/2017	...	6	2	3	5	0	0	0	0	0	0
10005269	Mr. David Thomson	1959	Phd	Widow	NaN	1	1	Cork	Ireland	3/24/2019	...	8	2	3	6	0	0	0	0	0	0
10005275	Mr. Anthony Jones	1956	Graduation	Married	85330.00	0	0	Killarney	Ireland	12/21/2018	...	13	8	11	4	0	0	0	0	0	0
10005278	Mr. Stewart Jones	1967	Graduation	Married	87513.00	0	0	Killarney	Ireland	2/24/2019	...	8	8	4	1	0	0	0	0	0	0
10005279	Mr. Stephen Jones	1984	Phd	Married	24507.00	1	0	Cork	Ireland	6/10/2018	...	8	3	2	8	0	0	0	0	0	1
10005287	Mr. Oliver Walsh	1960	Phd	Divorced	75599.00	0	1	Cork	Ireland	1/18/2019	...	13	3	6	6	0	1	0	0	0	0
10005314	Mrs. Jane Pullman	1966	Phd	Married	145834.35	0	1	Kinsale	Ireland	10/9/2017	...	1	0	1	1	0	0	0	0	0	0
10005324	Mr. Thomas Bailey	1961	Master	Married	59380.00	1	1	Killarney	Ireland	8/15/2018	...	8	3	4	6	0	0	0	0	0	0
10005351	Mr. Edward Pullman	1977	Phd	Married	63827.00	1	1	Kinsale	Ireland	4/12/2018	...	8	2	3	8	0	0	0	0	0	0
10005361	Mr. William Blake	1966	Phd	Divorced	54717.00	0	2	Kinsale	Ireland	10/22/2018	...	9	2	3	8	0	0	0	0	0	0
10005363	Miss Faith Allan	1983	Graduation	Single	26152.00	1	0	Kinsale	Ireland	12/22/2018	...	7	2	3	7	0	0	0	0	0	0
10005370	Mr. Charles Sutherland	1961	Graduation	Married	54256.00	1	1	Kinsale	Ireland	9/4/2018	...	9	2	4	7	0	0	0	0	0	0
10005415	Mr. Eric Ferguson	1954	Phd	Married	73912.00	1	1	Cork	Ireland	4/17/2019	...	12	4	5	6	0	0	0	0	0	0
10005422	Miss Madeleine Fisher	1959	Phd	Single	41078.00	1	1	Cork	Ireland	11/23/2017	...	7	2	3	8	0	0	0	0	0	0
10005431	Mr. Adrian Walker	1953	Phd	Married	37522.00	1	1	Kinsale	Ireland	9/8/2017	...	7	2	3	7	0	0	0	0	0	0
10005437	Mr. Steven Fraser	1980	Phd	Married	72090.00	0	1	Killarney	Ireland	3/17/2019	...	7	2	3	3	0	0	0	0	0	0
10005464	Mr. Christian Stewart	1987	Graduation	Married	63845.00	1	0	Kinsale	Ireland	5/1/2018	...	17	8	3	9	1	0	0	0	0	0
10005471	Mr. Leonard Russell	1980	Phd	Married	39745.00	1	0	Kinsale	Ireland	6/22/2019	...	8	2	3	4	0	0	0	0	0	0
10005486	Mr. Alexander Chapman	1985	Graduation	Married	33506.00	1	0	Killarney	Ireland	10/3/2018	...	8	2	3	7	0	0	0	0	0	0
10005487	Mr. Piers Parr	1980	Phd	Single	69430.00	1	1	Cork	Ireland	1/25/2018	...	10	3	4	7	0	0	0	0	0	0
10005502	Miss Michelle Coleman	1969	Graduation	Married	57825.00	0	1	Kinsale	Ireland	2/9/2018	...	8	2	3	8	0	0	0	0	0	0
10005510	Mrs. Pippa King	1976	Phd	Single	93993.00	0	1	Killarney	Ireland	11/29/2017	...	10	5	10	8	0	0	0	0	0	0
10005531	Mr. James Alsop	1960	Phd	Married	70664.00	0	1	Killarney	Ireland	8/21/2017	...	13	5	8	5	0	0	0	0	0	0
10005545	Mr. Simon Lewis	1969	Phd	Married	70785.00	1	1	Kinsale	Ireland	2/13/2018	...	12	4	4	7	0	0	0	0	0	0
10005565	Mr. Liam Henderson	1969	Phd	Married	79458.00	0	1	Killarney	Ireland	10/21/2017	...	14	3	7	7	0	1	0	0	0	0
10005576	Mrs. Mary Sutherland	1966	Phd	Married	64761.00	1	1	Kinsale	Ireland	2/4/2019	...	8	3	3	7	0	0	0	0	0	0
10005577	Mr. Victor Johnston	1976	Phd	Divorced	30072.00	1	1	Cork	Ireland	11/4/2017	...	8	2	4	5	0	0	0	0	0	0
10005587	Mrs. Sophie Turner	1982	Master	Married	41884.00	1	0	Killarney	Ireland	2/8/2019	...	7	2	3	8	0	0	0	0	0	0
10005597	Mr. Owen Hemmings	1980	Master	Divorced	40639.00	1	0	Killarney	Ireland	4/3/2018	...	7	2	3	8	0	0	0	0	0	0
10005606	Mr. David Mackenzie	1980	Phd	Married	60194.00	1	1	Kinsale	Ireland	7/16/2018	...	7	3	2	4	0	0	0	0	0	0
10005673	Mr. Stephen Kerr	1983	Phd	Single	41846.00	1	0	Killarney	Ireland	12/6/2017	...	7	2	2	8	1	0	0	0	0	0
10005677	Mr. Adrian McDonald	1957	Phd	Married	48707.00	1	1	Kinsale	Ireland	6/30/2019	...	8	2	3	5	0	0	0	0	0	0
10005683	Mr. Owen Roberts	1982	Phd	Single	65749.00	2	0	Cork	Ireland	10/21/2018	...	8	2	2	9	0	0	0	0	0	0
10005699	Miss Kimberly May	1965	Graduation	Single	54727.00	1	1	Kinsale	Ireland	2/3/2019	...	7	2	2	7	0	0	0	0	0	0
10005714	Mr. Justin Bower	1976	Phd	Single	48050.00	1	0	Cork	Ireland	6/1/2018	...	9	2	3	6	0	0	0	0	0	0
10005726	Mr. Paul Grant	1984	Phd	Married	40975.00	1	0	Killarney	Ireland	1/6/2018	...	10	3	4	8	0	0	0	0	0	0
10005731	Mr. Adrian Payne	1988	Phd	Single	42317.00	1	0	Killarney	Ireland	11/24/2018	...	8	3	4	5	0	0	0	0	0	0
10005747	Mr. Blake Churchill	1979	Phd	Married	51628.00	1	1	Cork	Ireland	12/30/2017	...	7	2	2	8	0	0	0	0	0	0
10005758	Mr. Liam Murray	1981	Phd	Married	74605.00	0	1	Killarney	Ireland	3/4/2019	...	14	4	6	7	0	0	0	0	0	0
10005782	Mr. Joshua Glover	1959	Phd	Married	29530.00	0	0	Cork	Ireland	4/14/2019	...	7	2	3	7	0	0	0	0	0	0
10005783	Miss Rebecca Simpson	1969	Phd	Divorced	79761.00	0	1	Killarney	Ireland	4/13/2018	...	17	4	7	8	0	0	0	0	0	0
10005787	Mr. Edward Hunter	1964	Master	Married	82748.00	0	2	Cork	Ireland	9/1/2018	...	16	6	7	6	0	1	0	0	0	0
10005794	Mrs. Emily Gill	1992	Phd	Married	66239.00	0	0	Killarney	Ireland	3/5/2018	...	12	8	10	6	0	0	0	0	0	0
10005835	Miss Felicity Slater	1975	Phd	Single	34452.00	1	0	Cork	Ireland	3/14/2019	...	7	2	2	8	0	0	0	0	0	0
10005837	Mr. Frank Slater	1975	Phd	Single	36152.00	1	1	Killarney	Ireland	10/22/2018	...	8	3	4	4	0	0	0	0	0	0
10005857	Mrs. Dorothy Reid	1988	Graduation	Married	NaN	1	0	Killarney	Ireland	1/6/2019	...	7	2	3	8	0	0	0	0	0	0
10005858	Mrs. Rachel Knox	1972	Phd	Married	79357.00	0	1	Cork	Ireland	12/20/2018	...	11	4	4	7	1	0	0	0	0	0
10005860	Mr. Alexander Gibson	1960	Phd	Single	73564.00	1	1	Kinsale	Ireland	12/9/2018	...	8	2	3	5	0	0	0	0	0	0
10005864	Mr. Jake Underwood	1979	Phd	Married	57683.00	0	1	Cork	Ireland	11/22/2017	...	11	4	6	5	0	0	0	0	0	0
10005899	Mrs. Donna Underwood	1993	Master	Single	43716.00	1	0	Killarney	Ireland	1/4/2019	...	7	2	3	7	0	0	0	0	0	0
10005915	Mr. Warren Bond	1968	Phd	Divorced	96765.00	0	0	Kinsale	Ireland	1/2/2019	...	8	6	6	1	0	0	0	0	0	0
10005921	Miss Megan Welch	1982	Master	Married	35262.00	1	0	Killarney	Ireland	12/31/2017	...	7	2	3	8	0	0	0	0	0	0
10005944	Mr. Dominic Ellison	1971	Phd	Married	56885.00	0	1	Killarney	Ireland	3/19/2019	...	7	2	3	6	0	0	0	0	0	0
10005945	Mr. Dylan Wright	1983	Phd	Divorced	97650.00	0	0	Cork	Ireland	2/13/2019	...	8	5	4	5	0	1	1	0	0	0
10005947	Mr. Harry James	1989	Phd	Single	35776.00	2	0	Cork	Ireland	4/8/2018	...	7	2	2	8	0	0	0	0	0	0
10005972	Mr. Jacob Metcalfe	1964	Phd	Married	92146.00	0	1	Kinsale	Ireland	9/13/2017	...	16	11	12	7	0	0	0	0	0	0
10006022	Miss Ella Buckland	1980	Master	Divorced	59585.00	1	1	Cork	Ireland	1/11/2019	...	7	3	2	7	1	0	0	0	0	0
10006037	Mr. Dominic Edmunds	1976	Phd	Married	44786.00	1	1	Cork	Ireland	11/4/2018	...	7	2	2	7	0	0	0	0	0	0
10006043	Miss Virginia Hodges	1964	Phd	Married	70234.00	0	1	Kinsale	Ireland	7/17/2018	...	10	5	5	9	0	0	0	0	0	0
10006062	Mrs. Donna Gill	1983	Phd	Married	51535.00	1	0	Killarney	Ireland	10/23/2017	...	15	3	7	9	0	0	0	0	0	0
10006078	Miss Maria Cornish	1947	Phd	Divorced	89181.00	0	0	Killarney	Ireland	6/27/2018	...	12	11	11	4	0	0	0	0	0	0
10006082	Miss Lisa Underwood	1987	Master	Married	28095.00	0	0	Killarney	Ireland	11/20/2018	...	7	2	2	5	0	0	0	0	0	0
10006085	Mr. Steven Nash	1953	Phd	Married	51175.00	1	1	Killarney	Ireland	1/4/2019	...	8	3	3	6	0	0	0	0	0	0
10006096	Mr. Lucas Springer	1978	Phd	Married	97807.00	0	0	Kinsale	Ireland	2/7/2019	...	11	13	5	2	1	0	1	1	0	0
10006112	Mr. Oliver Peters	1974	Phd	Divorced	83532.00	0	1	Kinsale	Ireland	6/16/2018	...	13	6	11	5	0	0	0	0	0	0
10006117	Mrs. Emma Fisher	1980	Phd	Married	70170.00	0	1	Kinsale	Ireland	6/18/2018	...	12	4	10	5	0	1	0	0	0	0
10006135	Mrs. Bella Vance	1954	Phd	Widow	74388.00	0	1	Cork	Ireland	3/4/2018	...	13	3	5	7	0	0	0	0	0	0
10006145	Mr. Connor McLean	1980	Phd	Married	40428.00	1	1	Cork	Ireland	1/4/2019	...	7	2	2	7	0	0	0	0	0	0
10006168	Mr. Brian Blake	1974	Phd	Married	58631.00	1	0	Cork	Ireland	12/13/2017	...	12	6	6	7	0	0	0	0	0	0
10006180	Mr. Stewart Nash	1983	Graduation	Divorced	58011.00	0	1	Kinsale	Ireland	6/28/2019	...	7	3	3	3	0	0	0	0	0	0
10006196	Mr. Evan Davidson	1984	Phd	Married	41207.00	1	0	Cork	Ireland	2/18/2019	...	8	2	3	5	0	0	0	0	0	0
10006211	Mr. Jason Hardacre	1972	Phd	Married	44301.00	1	1	Cork	Ireland	1/2/2019	...	7	2	2	7	0	0	0	0	0	0
10006268	Mr. Liam Mills	1970	Graduation	Married	41859.00	1	2	Cork	Ireland	3/24/2018	...	8	3	3	6	0	0	0	0	0	0
10006320	Mr. Oliver Skinner	1965	Phd	Divorced	86592.00	0	0	Killarney	Ireland	1/17/2018	...	10	6	8	2	0	0	0	0	0	0
10006323	Mr. William Nolan	1977	Graduation	Married	51617.00	2	0	Kinsale	Ireland	11/7/2018	...	7	3	2	7	0	0	0	0	0	0
10006362	Mrs. Pippa Lewis	1959	Phd	Married	62936.00	0	1	Killarney	Ireland	4/27/2019	...	12	4	5	7	0	0	0	0	0	0
10006366	Mr. Isaac Gibson	1981	Phd	Married	34223.00	1	0	Killarney	Ireland	11/5/2017	...	7	2	3	9	0	0	0	0	0	0
10006370	Miss Leah Cameron	1974	Phd	Widow	50153.00	1	1	Kinsale	Ireland	8/9/2018	...	7	2	3	7	1	0	0	0	0	0
10006392	Mr. James Turner	1957	Phd	Single	65981.00	1	1	Cork	Ireland	12/27/2017	...	8	2	4	7	0	0	0	0	0	0
10006397	Mr. Alexander Davies	1977	Phd	Married	63309.00	1	1	Killarney	Ireland	1/24/2019	...	8	2	3	5	0	0	0	0	0	0
10006415	Mrs. Bernadette Young	1977	Phd	Married	45442.00	1	1	Killarney	Ireland	3/23/2018	...	8	2	3	8	0	0	0	0	0	0
10006429	Miss Carolyn Lewis	1973	Phd	Married	77570.00	0	1	Kinsale	Ireland	3/27/2019	...	14	7	8	5	0	0	0	0	0	0
10006435	Mr. Jonathan Clarkson	1991	Phd	Married	61540.00	1	0	Kinsale	Ireland	12/7/2018	...	7	2	2	5	0	0	0	0	0	0
10006442	Miss Anna Coleman	1960	Phd	Single	59513.00	1	1	Kinsale	Ireland	10/12/2018	...	8	2	3	7	0	0	0	0	0	0
10006444	Mrs. Jennifer King	1968	Phd	Married	62462.00	0	1	Killarney	Ireland	9/24/2017	...	17	3	6	9	0	0	0	0	0	0
10006463	Miss Julia Scott	1978	Phd	Married	44861.00	1	1	Cork	Ireland	2/21/2018	...	8	3	2	7	0	0	0	0	0	0
10006465	Mr. Peter Murray	1983	Graduation	Divorced	52040.00	1	1	Cork	Ireland	1/2/2018	...	8	3	3	8	0	0	0	0	0	0
10006481	Mr. Peter Wright	1971	Phd	Married	56856.00	1	0	Kinsale	Ireland	5/17/2018	...	8	2	4	7	0	0	0	0	0	0
10006483	Miss Faith McDonald	1994	Graduation	Married	113743.00	0	0	Kinsale	Ireland	10/13/2018	...	11	9	7	2	0	0	0	0	0	0
10006501	Mr. Lucas Paterson	1980	Phd	Married	60004.00	1	0	Killarney	Ireland	6/30/2018	...	10	2	4	8	0	0	0	0	0	0
10006536	Mrs. Maria Hardacre	1992	Phd	Married	70283.00	0	0	Killarney	Ireland	2/3/2018	...	14	9	13	5	0	0	0	0	0	0
10006537	Mr. Harry Ince	1959	Graduation	Married	54826.00	1	1	Cork	Ireland	11/7/2018	...	8	3	3	6	0	0	0	0	0	0
10006557	Mr. Peter Hardacre	1971	Master	Married	54789.00	1	1	Killarney	Ireland	5/4/2019	...	6	2	3	5	0	0	0	0	0	0
10006564	Mr. Evan Thomson	1953	Phd	Divorced	52077.00	2	1	Killarney	Ireland	2/3/2019	...	8	2	4	5	0	0	0	0	0	0
10006578	Mr. Benjamin Hunter	1990	Master	Single	61786.00	1	0	Killarney	Ireland	4/26/2019	...	7	2	4	6	0	0	0	0	0	0
10006584	Mr. Dan McGrath	1962	Phd	Married	72773.00	0	1	Cork	Ireland	4/17/2019	...	11	6	4	4	0	0	0	0	0	0
10006596	Mr. Dylan Kelly	1960	Master	Married	57505.00	0	2	Kinsale	Ireland	4/17/2018	...	8	2	3	5	0	0	0	0	0	0
10006616	Mr. Gavin Hamilton	1989	Phd	Married	104123.00	0	0	Killarney	Ireland	6/26/2019	...	11	7	12	1	0	0	0	0	0	0
10006626	Mr. Connor Alsop	1973	Graduation	Married	62371.00	1	1	Killarney	Ireland	7/13/2018	...	6	3	2	4	0	0	0	0	0	0
10006644	Miss Hannah Arnold	1963	Phd	Married	127549.95	0	1	Kinsale	Ireland	8/31/2017	...	22	1	1	1	0	0	0	0	0	0
10006655	Mr. Sebastian Kelly	1974	Phd	Married	81082.00	0	2	Kinsale	Ireland	10/26/2017	...	17	6	11	8	0	0	0	0	0	0
10006656	Miss Abigail Knox	1987	Phd	Married	NaN	1	0	Cork	Ireland	2/27/2019	...	9	3	3	6	0	0	0	0	0	0
10006660	Mrs. Audrey Hudson	1974	Phd	Single	77454.00	1	1	Cork	Ireland	6/8/2019	...	7	2	2	5	0	0	0	0	0	0
10006675	Mr. Steven Quinn	1958	Phd	Married	67771.00	1	1	Killarney	Ireland	4/27/2018	...	9	3	5	5	0	0	0	0	0	0
10006690	Mr. Adrian Russell	1965	Phd	Married	58950.00	1	1	Cork	Ireland	11/30/2018	...	8	3	5	6	0	0	0	0	0	0
10006702	Mr. Adrian May	1954	Phd	Married	77174.00	0	1	Killarney	Ireland	12/22/2017	...	12	3	6	7	0	0	0	0	0	0
10006728	Mr. Robert MacDonald	1989	Phd	Single	74804.00	0	0	Killarney	Ireland	3/4/2018	...	10	10	13	2	0	1	0	0	0	0
10006746	Mr. Piers Reid	1964	Graduation	Divorced	51110.00	1	1	Cork	Ireland	8/4/2017	...	9	2	3	9	0	0	0	0	0	0
10006766	Mr. Alexander Grant	1985	Phd	Married	79413.00	0	0	Kinsale	Ireland	9/13/2017	...	15	5	13	6	0	0	0	0	0	0
10006776	Mr. Peter Welch	1966	Phd	Married	36870.00	1	1	Killarney	Ireland	9/25/2017	...	9	2	3	9	0	0	0	0	0	0
10006799	Mr. Justin Kelly	1950	Graduation	Married	24370.00	0	0	Kinsale	Ireland	9/7/2017	...	7	2	2	7	0	0	0	0	0	0
10006814	Miss Andrea Ball	1965	Phd	Divorced	79205.00	0	1	Cork	Ireland	12/22/2017	...	9	4	11	8	0	1	0	0	1	0
10006832	Mrs. Anne Ellison	1973	Phd	Married	73784.00	0	0	Kinsale	Ireland	8/9/2018	...	16	12	8	6	0	0	0	0	0	0
10006863	Mr. Brandon Hughes	1955	Phd	Married	37885.00	0	1	Killarney	Ireland	4/9/2019	...	8	3	4	2	0	0	0	0	0	0
10006917	Mr. Leonard Forsyth	1986	Phd	Married	25980.00	1	0	Kinsale	Ireland	9/25/2018	...	7	2	2	7	0	0	0	0	0	0
10006935	Mr. Alexander Fisher	1988	Phd	Married	55588.00	1	0	Cork	Ireland	3/23/2019	...	7	2	2	8	0	0	0	0	0	0
10006962	Mr. Andrew Short	1988	Phd	Married	118870.00	1	0	Cork	Ireland	2/19/2018	...	12	7	11	8	1	0	1	0	0	0
10006970	Miss Anne Mills	1981	Phd	Married	51806.00	1	1	Killarney	Ireland	12/27/2017	...	7	2	2	6	0	0	0	0	0	0
10006971	Miss Samantha James	1980	Phd	Married	46113.00	2	1	Cork	Ireland	4/20/2019	...	8	2	3	6	0	0	0	0	0	0
10006973	Mr. Christopher Alsop	1961	Phd	Married	77448.00	0	1	Kinsale	Ireland	1/21/2018	...	17	5	7	8	0	0	0	0	0	0
10006980	Mr. Neil Clark	1967	Phd	Married	80841.00	1	1	Kinsale	Ireland	4/6/2018	...	15	4	8	7	0	1	0	0	0	0
10006984	Mr. Harry Martin	1983	Phd	Married	33829.00	1	1	Cork	Ireland	9/16/2018	...	7	2	3	8	0	0	0	0	0	0
10006989	Mr. Kevin Jones	1982	Phd	Married	63025.00	1	1	Killarney	Ireland	6/3/2019	...	7	2	3	3	0	0	0	0	0	0
10006998	Mrs. Leah White	1969	Phd	Married	79437.00	0	1	Cork	Ireland	1/20/2018	...	12	12	4	4	0	0	0	0	0	0
10007000	Miss Amanda Miller	1975	Phd	Married	63751.00	0	1	Cork	Ireland	2/1/2019	...	8	4	2	4	1	0	0	0	0	0
10007030	Mr. Steven Hodges	1958	Phd	Single	63022.00	0	1	Cork	Ireland	10/17/2017	...	13	3	5	8	0	0	0	0	0	0
10007039	Mr. Justin Paterson	1962	Phd	Divorced	44806.00	1	1	Cork	Ireland	4/6/2019	...	8	2	3	8	0	0	0	0	0	0
10007064	Mrs. Grace Pullman	1954	Graduation	Single	51397.00	0	0	Killarney	Ireland	10/2/2017	...	9	4	3	6	0	0	0	0	0	0
10007065	Mr. Colin King	1971	Phd	Married	51804.00	1	1	Cork	Ireland	5/24/2019	...	8	2	3	7	0	0	0	0	0	0
10007086	Mrs. Sonia Short	1990	Phd	Married	62697.00	0	0	Cork	Ireland	3/6/2019	...	10	7	7	3	0	0	0	0	0	0
10007092	Mr. Peter Scott	1977	Phd	Single	91266.00	0	1	Cork	Ireland	5/13/2018	...	13	9	9	5	0	0	0	0	0	0
10007104	Miss Dorothy Morrison	1969	Graduation	Married	46273.00	1	1	Cork	Ireland	4/25/2019	...	7	3	3	5	0	0	0	0	0	0
10007108	Mr. Alan Russell	1953	Phd	Married	46643.00	0	1	Cork	Ireland	11/23/2018	...	7	3	3	6	0	0	0	0	0	0
10007112	Mr. Piers Jackson	1982	Graduation	Married	17499.00	1	0	Kinsale	Ireland	9/3/2018	...	9	2	3	9	0	0	0	0	0	0
10007113	Mr. Simon White	1994	Graduation	Married	31911.00	1	0	Cork	Ireland	4/24/2018	...	7	2	2	6	0	0	0	0	0	0
10007169	Mr. Andrew Duncan	1977	Phd	Single	99741.00	0	1	Cork	Ireland	12/20/2017	...	11	6	5	4	0	0	0	0	0	0
10007181	Miss Penelope Springer	1990	Graduation	Married	52604.00	1	0	Cork	Ireland	4/4/2019	...	8	2	3	6	0	0	0	0	0	0
10007182	Mrs. Kylie Russell	1976	Phd	Married	69575.00	1	0	Kinsale	Ireland	9/19/2018	...	11	3	8	4	0	0	0	0	0	0
10007184	Mrs. Joan Paterson	1957	Phd	Married	58831.00	0	1	Killarney	Ireland	5/12/2018	...	12	4	5	8	0	0	0	0	0	0
10007216	Miss Penelope Mathis	1981	Phd	Married	39480.00	1	0	Kinsale	Ireland	11/19/2018	...	8	2	3	7	0	0	0	0	0	0
10007223	Mr. Justin Murray	1986	Master	Married	85044.00	2	0	Kinsale	Ireland	4/15/2019	...	8	4	9	2	0	0	0	0	0	0
10007256	Mr. Justin Vaughan	1981	Phd	Single	93252.00	0	1	Killarney	Ireland	6/22/2018	...	9	5	4	6	0	1	1	0	1	0
10007277	Miss Ava Langdon	1970	Master	Married	74414.00	1	1	Kinsale	Ireland	3/19/2019	...	7	3	2	4	0	0	0	0	0	0
10007286	Mr. Sam Poole	1969	Phd	Single	68548.00	1	1	Cork	Ireland	5/27/2018	...	6	2	3	5	0	0	0	0	0	0
10007295	Mrs. Alison Bailey	1958	Graduation	Married	88056.00	1	1	Cork	Ireland	6/18/2019	...	13	7	3	6	0	1	0	0	0	1
10007321	Mr. Lucas Skinner	1960	Phd	Married	41938.00	1	1	Kinsale	Ireland	7/7/2018	...	8	2	3	8	0	0	0	0	0	0
10007323	Miss Victoria North	1964	Phd	Married	59100.00	0	1	Killarney	Ireland	3/8/2018	...	14	4	7	8	0	1	0	0	0	0
10007326	Mrs. Diana Mathis	1981	Phd	Married	33810.00	0	1	Kinsale	Ireland	7/23/2018	...	7	2	3	6	0	0	0	0	0	0
10007339	Mrs. Sue Randall	1966	Phd	Married	136171.95	0	1	Killarney	Ireland	8/17/2018	...	1	0	0	1	0	0	0	0	0	0
10007341	Mr. Stewart Marshall	1979	Phd	Single	65232.00	1	1	Cork	Ireland	10/19/2018	...	10	4	3	8	0	0	0	0	0	0
10007353	Mr. Lucas Brown	1955	Phd	Married	60090.00	0	1	Killarney	Ireland	5/17/2018	...	7	2	2	7	0	0	0	0	0	0
10007381	Mr. Evan Piper	1953	Phd	Married	68245.00	0	1	Cork	Ireland	6/24/2019	...	7	3	3	2	0	1	0	0	0	0
10007395	Mr. Max Wright	1966	Graduation	Married	40176.00	1	1	Kinsale	Ireland	3/19/2019	...	7	2	3	6	0	0	0	0	0	0
10007403	Mr. Paul Buckland	1974	Phd	Married	4476.30	0	1	Killarney	Ireland	12/20/2017	...	1	0	0	13	0	0	0	0	0	0
10007418	Mr. Joseph Wright	1959	Master	Married	39562.00	1	1	Killarney	Ireland	3/13/2018	...	6	2	3	6	0	0	0	0	0	0
10007434	Mr. Jack Gray	1975	Graduation	Single	47686.00	1	0	Cork	Ireland	12/12/2018	...	8	2	4	6	0	0	0	0	0	0
10007455	Mr. Carl Knox	1985	Master	Single	60040.00	1	1	Kinsale	Ireland	5/24/2019	...	8	2	2	8	0	0	0	0	0	0
10007481	Mr. Jack Ogden	1958	Phd	Divorced	53134.00	1	1	Kinsale	Ireland	4/8/2018	...	7	2	3	6	0	0	0	0	0	0
10007500	Miss Felicity Hughes	1984	Graduation	Single	69097.00	2	1	Kinsale	Ireland	6/30/2019	...	7	2	2	7	1	0	0	0	0	0
10007504	Miss Audrey Wallace	1955	Master	Married	62362.00	1	1	Kinsale	Ireland	12/15/2018	...	10	3	4	8	1	0	0	0	0	0
10007505	Mr. Keith Pullman	1986	Phd	Single	95263.00	0	0	Cork	Ireland	4/10/2019	...	8	5	6	1	0	0	0	0	0	0
10007506	Mr. Brandon Quinn	1978	Phd	Single	61943.00	0	1	Killarney	Ireland	1/22/2019	...	8	2	4	6	0	0	0	0	0	0
10007510	Mr. Eric Mackenzie	1970	Phd	Married	NaN	0	1	Kinsale	Ireland	10/9/2017	...	11	7	8	8	0	0	0	0	1	0
10007516	Mrs. Emma May	1978	Master	Married	19603.00	1	0	Cork	Ireland	1/7/2019	...	9	3	3	5	0	0	0	0	0	0
10007557	Mr. Carl Mackay	1973	Master	Married	99247.00	0	1	Cork	Ireland	6/27/2019	...	11	5	4	2	0	0	0	0	0	0
10007583	Miss Michelle Hudson	1957	Phd	Widow	39782.00	1	2	Kinsale	Ireland	3/24/2018	...	10	3	4	8	0	0	0	0	0	0
10007605	Mr. Sean Smith	1979	Graduation	Married	85237.00	0	0	Cork	Ireland	4/12/2019	...	9	5	13	2	0	0	0	0	0	0
10007611	Mr. Ian Campbell	1977	Phd	Married	56525.00	1	0	Killarney	Ireland	3/24/2019	...	7	2	3	7	0	0	0	0	0	0
10007612	Mr. Sebastian Manning	1976	Phd	Married	83985.00	0	1	Killarney	Ireland	4/23/2019	...	10	4	10	3	0	0	0	0	0	0
10007623	Miss Wendy Avery	1949	Phd	Married	68750.00	0	0	Kinsale	Ireland	10/2/2017	...	10	6	10	8	0	1	1	0	0	0
10007627	Mr. Leonard Mackenzie	1952	Phd	Married	78249.00	0	1	Cork	Ireland	3/7/2019	...	13	3	6	6	0	0	0	0	0	0
10007629	Mr. Sam Lee	1969	Phd	Married	72096.00	1	1	Killarney	Ireland	7/28/2018	...	8	2	2	8	0	0	0	0	0	0
10007648	Mr. Carl Marshall	1957	Phd	Married	72175.00	0	1	Killarney	Ireland	2/17/2019	...	7	2	3	7	0	0	0	0	0	0
10007664	Mr. Robert Miller	1969	Phd	Married	59984.00	1	1	Killarney	Ireland	11/11/2017	...	10	4	3	8	0	0	0	0	0	0
10007694	Mr. Harry Powell	1963	Phd	Married	61999.00	0	2	Kinsale	Ireland	3/7/2018	...	8	2	4	7	0	0	0	0	0	0
10007704	Miss Caroline Hill	1952	Phd	Married	51257.00	1	1	Kinsale	Ireland	1/28/2019	...	7	2	3	7	0	0	0	0	0	0
10007740	Mr. Luke Allan	1988	Phd	Married	92296.00	0	0	Killarney	Ireland	7/13/2018	...	13	8	11	4	1	0	1	0	0	0
10007770	Mr. Alan Springer	1962	Master	Married	51997.00	1	1	Kinsale	Ireland	11/4/2017	...	8	2	3	8	0	0	0	0	0	1
10007776	Mr. Alexander Howard	1949	Phd	Married	94787.00	0	0	Kinsale	Ireland	4/11/2018	...	13	10	12	4	0	1	1	0	0	0
10007785	Mr. Keith Scott	1981	Phd	Divorced	44243.00	1	1	Kinsale	Ireland	5/29/2018	...	8	3	2	6	0	0	0	0	0	0
10007788	Mr. Trevor Poole	1965	Phd	Single	53865.00	1	1	Kinsale	Ireland	3/17/2019	...	9	2	4	6	0	0	0	0	0	0
10007816	Mr. Isaac Jones	1970	Phd	Married	46932.00	1	1	Killarney	Ireland	9/16/2018	...	7	2	3	4	0	0	0	0	0	0
10007834	Mr. Connor Martin	1960	Phd	Married	27061.00	1	1	Killarney	Ireland	8/2/2018	...	7	2	3	7	0	0	0	0	0	0
10007839	Mr. Jonathan Burgess	1954	Phd	Widow	39295.00	1	1	Cork	Ireland	3/19/2019	...	8	2	2	8	0	0	0	0	0	0
10007863	Mrs. Dorothy Scott	1964	Phd	Married	29574.00	0	1	Killarney	Ireland	11/1/2018	...	9	3	3	7	0	0	0	0	0	0
10007871	Mr. John Scott	1976	Phd	Married	44241.00	1	0	Cork	Ireland	1/4/2019	...	7	2	2	6	1	0	0	0	0	0
10007888	Mr. Justin Turner	1966	Phd	Married	50430.00	1	1	Killarney	Ireland	2/21/2019	...	7	2	2	6	0	0	0	0	0	0
10007904	Mr. Tim Peters	1974	Phd	Married	54824.00	1	0	Killarney	Ireland	4/22/2018	...	8	2	4	6	0	0	0	0	0	0
10007910	Mr. Sean Sutherland	1962	Phd	Married	89237.00	0	1	Cork	Ireland	9/25/2018	...	13	6	5	5	0	1	0	1	0	0
10007925	Mr. Kevin Lambert	1985	Phd	Married	19928.00	1	0	Cork	Ireland	10/1/2018	...	7	2	3	7	0	0	0	0	0	0
10007932	Mrs. Nicola Hemmings	1957	Master	Single	43828.00	1	1	Kinsale	Ireland	3/10/2018	...	7	2	3	4	0	0	0	0	0	0
10007933	Mr. Dan Howard	1970	Phd	Divorced	52852.00	1	1	Cork	Ireland	12/14/2018	...	7	3	2	5	0	0	0	0	0	0
10007951	Mr. Benjamin Turner	1973	Phd	Single	52135.00	0	1	Kinsale	Ireland	4/28/2018	...	7	2	3	5	0	0	0	0	0	0
10007963	Mr. Michael Newman	1977	Master	Married	23999.00	1	0	Cork	Ireland	7/21/2018	...	7	2	3	7	0	0	0	0	0	0
10007964	Mr. Victor Nash	1972	Phd	Single	49782.00	1	0	Kinsale	Ireland	6/26/2019	...	7	2	3	8	0	0	0	0	0	0
10007981	Mr. James Johnston	1970	Phd	Married	66821.00	1	1	Killarney	Ireland	6/21/2019	...	7	2	4	5	0	1	0	0	0	0
10007988	Mr. Max Underwood	1974	Phd	Married	73220.00	2	1	Kinsale	Ireland	4/29/2018	...	12	3	4	8	0	0	0	0	0	0
10007999	Mr. Trevor North	1979	Graduation	Single	84145.00	0	1	Kinsale	Ireland	5/1/2018	...	17	6	9	7	0	0	0	0	0	0
10008000	Mr. Sam Hamilton	1974	Phd	Single	36816.00	1	0	Killarney	Ireland	5/7/2019	...	8	2	3	6	0	0	0	0	0	0
10008023	Mrs. Emily Hart	1974	Phd	Married	58951.00	1	0	Killarney	Ireland	3/22/2019	...	7	2	2	4	0	0	0	0	0	0
10008039	Miss Leah Cornish	1974	Phd	Married	28397.00	1	0	Cork	Ireland	2/27/2019	...	8	3	2	8	1	0	0	0	0	0
10008055	Mr. Andrew Bell	1978	Phd	Divorced	102164.00	0	0	Cork	Ireland	4/9/2019	...	9	13	7	1	0	0	1	1	0	0
10008062	Miss Angela Abraham	1957	Graduation	Married	98522.00	0	0	Cork	Ireland	11/14/2018	...	9	13	8	1	0	0	0	0	0	0
10008065	Mr. Gavin Morrison	1971	Phd	Married	51524.00	1	1	Cork	Ireland	11/30/2018	...	8	2	3	7	0	0	0	0	0	1
10008110	Mr. Jake Alsop	1958	Phd	Married	78417.00	1	1	Cork	Ireland	9/3/2018	...	9	3	4	5	0	0	0	0	0	0
10008115	Mr. William Sutherland	1962	Phd	Married	65734.00	1	1	Cork	Ireland	10/15/2017	...	7	2	3	6	0	0	0	0	0	0
10008117	Miss Audrey Newman	1963	Phd	Married	65904.00	0	1	Cork	Ireland	3/1/2018	...	15	5	6	8	0	0	0	0	0	0
10008122	Mr. Colin Marshall	1971	Phd	Married	57490.00	0	1	Kinsale	Ireland	3/5/2018	...	9	3	7	5	0	0	0	0	0	0
10008128	Mr. Dan Springer	1959	Phd	Widow	61902.00	1	1	Killarney	Ireland	10/14/2018	...	8	4	2	5	0	0	0	0	0	0
10008145	Mr. Carl Hemmings	1962	Phd	Single	58761.00	1	1	Killarney	Ireland	3/16/2018	...	7	3	3	5	0	0	0	0	0	0
10008149	Mr. Neil Hemmings	1973	Graduation	Married	41562.00	1	1	Cork	Ireland	11/21/2017	...	6	2	3	6	0	0	0	0	0	0
10008155	Mrs. Abigail Ince	1978	Phd	Married	41722.00	1	0	Kinsale	Ireland	4/25/2018	...	6	2	3	6	1	0	0	0	0	0
10008156	Mr. William Black	1983	Phd	Married	NaN	1	0	Killarney	Ireland	6/19/2018	...	9	2	3	7	0	0	0	0	0	0
10008170	Mr. Gavin Taylor	1982	Graduation	Single	27283.00	1	0	Cork	Ireland	6/3/2018	...	8	2	4	8	0	0	0	0	0	0
10008184	Mrs. Gabrielle Ince	1968	Phd	Married	44223.00	0	1	Cork	Ireland	10/14/2017	...	8	3	4	7	0	0	0	0	0	0
10008190	Mr. Stewart Randall	1981	Phd	Married	84259.00	0	1	Kinsale	Ireland	9/18/2017	...	14	10	10	5	0	0	0	0	0	0
10008192	Mr. Adrian Davies	1982	Phd	Married	47607.00	1	0	Kinsale	Ireland	7/18/2018	...	6	2	3	4	0	0	0	0	0	0
10008203	Mr. Austin Pullman	1991	Phd	Married	39030.00	1	0	Cork	Ireland	9/3/2017	...	7	2	3	9	0	0	0	0	0	0
10008210	Mr. Eric Smith	1978	Master	Single	34765.00	1	0	Killarney	Ireland	6/10/2019	...	8	2	3	8	0	0	0	0	0	0
10008215	Mr. Peter Short	1959	Phd	Divorced	53934.00	0	1	Kinsale	Ireland	2/17/2019	...	7	3	2	5	1	0	0	0	0	0
10008238	Mr. Peter Burgess	1968	Phd	Married	85418.00	0	1	Cork	Ireland	11/26/2017	...	16	5	7	7	0	0	0	0	0	0
10008250	Mr. Tim Mitchell	1986	Phd	Married	77702.00	0	0	Kinsale	Ireland	1/26/2018	...	16	4	12	6	0	0	0	1	0	0
10008258	Mr. Oliver Turner	1963	Phd	Married	157942.05	1	1	Cork	Ireland	1/14/2018	...	30	0	0	0	0	0	0	0	0	0
10008279	Mr. Dylan Mathis	1973	Phd	Married	97615.00	0	1	Killarney	Ireland	5/16/2019	...	12	5	12	2	0	0	0	0	0	0
10008280	Mr. Isaac Hamilton	1963	Graduation	Married	56000.00	1	1	Kinsale	Ireland	2/5/2018	...	10	6	3	7	1	0	0	0	0	0
10008286	Mrs. Rose Underwood	1979	Phd	Married	46177.00	1	1	Killarney	Ireland	2/13/2019	...	7	2	3	6	0	0	0	0	0	0
10008300	Mrs. Wanda King	1976	Phd	Married	48460.00	1	1	Killarney	Ireland	5/9/2019	...	8	2	3	6	0	0	0	0	0	0
10008350	Mr. Benjamin Murray	1964	Phd	Single	44838.00	1	1	Cork	Ireland	8/9/2017	...	9	3	4	9	0	0	0	0	0	0
10008356	Mrs. Julia Allan	1978	Phd	Married	49235.00	1	1	Cork	Ireland	6/3/2019	...	9	2	3	8	0	0	0	0	0	0
10008360	Mr. Ian Langdon	1970	Phd	Married	55586.00	1	1	Killarney	Ireland	1/26/2018	...	8	2	3	8	0	0	0	0	0	0
10008361	Mr. Paul Ferguson	1962	Phd	Single	64023.00	0	1	Killarney	Ireland	8/25/2017	...	17	3	7	8	0	0	0	0	0	0
10008368	Mr. Richard Turner	1964	Phd	Married	59312.00	0	1	Kinsale	Ireland	3/13/2019	...	6	2	3	5	0	0	0	0	0	0
10008373	Miss Jane Piper	1978	Phd	Divorced	60591.00	1	1	Kinsale	Ireland	9/23/2017	...	12	3	4	9	0	0	0	0	0	0
10008377	Mrs. Lisa Mills	1976	Phd	Married	62227.00	1	1	Killarney	Ireland	9/17/2017	...	12	3	8	8	1	0	0	0	0	0
10008400	Mr. Stephen MacDonald	1974	Phd	Married	28606.00	1	1	Cork	Ireland	3/25/2019	...	7	3	3	3	0	0	0	0	0	0
10008408	Mr. Jake Ferguson	1978	Phd	Divorced	107106.00	0	0	Killarney	Ireland	12/29/2018	...	13	10	4	4	0	0	1	0	0	0
10008409	Mr. Leonard Martin	1980	Phd	Widow	49699.00	0	0	Cork	Ireland	5/23/2018	...	15	3	6	8	0	1	0	0	0	0
10008415	Mr. Alexander Nash	1963	Graduation	Married	42709.00	1	1	Killarney	Ireland	11/26/2018	...	7	2	4	4	0	0	0	0	0	0
10008431	Mr. Warren Kerr	1985	Phd	Married	27879.00	1	0	Killarney	Ireland	4/9/2019	...	9	2	4	5	0	0	0	0	0	0
10008439	Mr. Brian Glover	1977	Phd	Divorced	37127.00	1	1	Cork	Ireland	8/13/2018	...	9	3	3	8	1	0	0	0	0	0
10008448	Mr. Dominic Parr	1977	Phd	Married	62428.00	0	1	Cork	Ireland	8/15/2017	...	12	4	9	7	0	0	0	0	0	0
10008453	Mrs. Faith Ross	1981	Phd	Married	43444.00	1	1	Killarney	Ireland	6/16/2019	...	7	2	2	7	0	0	0	0	0	0
10008461	Miss Megan Roberts	1970	Phd	Married	5193.40	0	0	Cork	Ireland	11/26/2017	...	2	1	0	11	1	0	0	1	0	0
10008477	Mr. Gavin Mackenzie	1981	Phd	Widow	83332.00	1	0	Cork	Ireland	12/10/2018	...	9	3	6	4	0	0	0	0	0	0
10008491	Mr. David Paige	1984	Master	Married	83407.00	0	1	Kinsale	Ireland	5/12/2018	...	13	5	5	6	0	0	0	0	0	0
10008496	Mr. Matt Avery	1958	Phd	Married	77101.00	0	1	Cork	Ireland	4/4/2018	...	14	4	8	7	0	0	0	0	0	0
10008498	Mr. Evan Abraham	1967	Phd	Widow	133197.45	0	1	Kinsale	Ireland	9/14/2017	...	2	0	1	1	1	0	0	0	0	0
10008526	Mrs. Vanessa Arnold	1980	Phd	Married	56316.00	1	1	Kinsale	Ireland	7/11/2018	...	7	2	2	6	0	0	0	0	0	0
10008536	Mr. Brian Hunter	1980	Graduation	Married	55691.00	0	1	Cork	Ireland	7/11/2018	...	7	2	3	5	0	0	0	0	0	0
10008568	Mr. Tim Hamilton	1969	Phd	Married	59767.00	0	1	Kinsale	Ireland	4/27/2019	...	8	2	4	7	0	0	0	0	0	0
10008569	Mr. Stephen Alsop	1989	Phd	Married	42220.00	1	0	Kinsale	Ireland	12/22/2018	...	7	2	3	5	0	0	0	0	0	0
10008575	Mrs. Abigail Short	1977	Phd	Single	87580.00	0	2	Killarney	Ireland	3/30/2019	...	15	4	9	5	0	0	0	0	0	0
10008594	Miss Emma Campbell	1959	Phd	Married	51269.00	1	1	Kinsale	Ireland	3/31/2019	...	9	2	3	7	0	0	0	0	0	0
10008598	Mr. Max Peters	1960	Phd	Widow	81068.00	0	1	Kinsale	Ireland	10/21/2017	...	13	6	6	6	0	1	0	1	0	0
10008601	Mr. Luke Payne	1977	Graduation	Married	82157.00	0	0	Kinsale	Ireland	9/30/2017	...	9	8	8	3	0	0	0	0	0	0
10008617	Mr. Justin Clark	1976	Phd	Married	102659.00	0	1	Cork	Ireland	3/29/2018	...	17	6	9	5	0	0	0	1	0	0
10008637	Mr. Cameron Peters	1958	Phd	Married	91151.00	0	1	Killarney	Ireland	4/5/2018	...	8	4	12	7	0	1	1	0	0	0
10008656	Mr. Steven Churchill	1959	Graduation	Married	41003.00	1	1	Kinsale	Ireland	7/12/2018	...	7	3	3	5	0	0	0	0	0	0
10008661	Mrs. Pippa Russell	1959	Phd	Married	46954.00	1	1	Killarney	Ireland	7/7/2018	...	7	2	3	5	0	0	0	0	0	0
10008686	Mrs. Ella Metcalfe	1984	Phd	Married	40473.00	1	0	Killarney	Ireland	6/10/2019	...	7	2	3	7	0	0	0	0	0	0
10008712	Mr. Lucas Reid	1953	Phd	Married	70668.00	1	1	Kinsale	Ireland	6/10/2019	...	9	2	3	8	1	0	0	0	0	0
10008721	Mr. Joshua Walsh	1965	Phd	Married	52337.00	0	1	Cork	Ireland	12/16/2018	...	10	4	4	6	0	0	0	0	0	0
10008722	Mr. Owen Cameron	1967	Phd	Single	52243.00	0	1	Cork	Ireland	9/11/2018	...	7	3	4	4	0	0	0	0	0	0
10008729	Miss Jan Alsop	1992	Phd	Married	59339.00	1	0	Killarney	Ireland	3/5/2019	...	9	2	3	7	0	0	0	0	0	0
10008742	Mr. Steven Henderson	1958	Graduation	Married	57226.00	1	1	Cork	Ireland	5/22/2018	...	9	3	3	7	0	0	0	0	0	0
10008749	Mrs. Jan Peters	1960	Phd	Married	54982.00	0	1	Kinsale	Ireland	4/12/2019	...	7	2	2	6	0	0	0	0	0	0
10008761	Mr. William Howard	1956	Phd	Divorced	100133.25	0	0	Cork	Ireland	8/8/2018	...	1	1	1	0	0	1	1	0	0	0
10008763	Mrs. Michelle Gibson	1991	Phd	Married	51947.00	1	0	Kinsale	Ireland	12/31/2018	...	8	3	2	7	0	0	0	0	0	0
10008787	Mr. Piers MacDonald	1956	Phd	Widow	100100.00	0	0	Cork	Ireland	1/13/2019	...	9	8	12	1	0	0	1	0	0	0
10008795	Mr. Andrew Davidson	1986	Phd	Divorced	39569.00	1	0	Killarney	Ireland	12/8/2018	...	7	2	3	6	0	0	0	0	0	0
10008829	Mrs. Gabrielle Jackson	1973	Phd	Married	27236.00	1	0	Killarney	Ireland	3/25/2019	...	7	2	2	8	0	0	0	0	0	0
10008873	Mr. Oliver Vance	1951	Phd	Single	69276.00	0	0	Kinsale	Ireland	8/30/2017	...	16	5	13	7	0	0	0	0	0	0
10008886	Mr. Edward Butler	1960	Master	Married	45627.00	1	1	Killarney	Ireland	2/12/2018	...	7	2	2	7	0	0	0	0	0	0
10008897	Mr. Alan Quinn	1982	Phd	Divorced	52886.00	1	0	Cork	Ireland	5/26/2018	...	10	4	3	7	1	0	0	0	0	0
10008905	Mr. Luke Springer	1953	Phd	Divorced	85819.00	0	1	Kinsale	Ireland	4/19/2019	...	17	5	10	7	0	1	0	1	1	0
10008910	Mrs. Virginia Ferguson	1969	Graduation	Single	53192.00	1	1	Killarney	Ireland	5/3/2019	...	8	3	3	5	0	0	0	0	0	0
10008923	Mrs. Penelope Ball	1980	Phd	Married	61640.00	1	1	Killarney	Ireland	9/23/2018	...	6	2	3	4	0	0	0	0	0	0
10008933	Mr. Joe Harris	1981	Phd	Married	102732.00	0	1	Kinsale	Ireland	8/4/2018	...	15	7	13	8	0	1	0	0	0	0
10008951	Mr. Carl Piper	1972	Phd	Married	53643.00	0	1	Kinsale	Ireland	10/15/2018	...	8	3	5	6	0	0	0	0	0	0
10008952	Mrs. Fiona Forsyth	1970	Phd	Divorced	52304.00	0	0	Killarney	Ireland	5/11/2018	...	6	2	3	4	0	0	0	0	0	0
10008972	Miss Hannah Rees	1973	Phd	Single	84592.00	1	1	Cork	Ireland	4/28/2019	...	7	2	3	4	0	0	0	0	0	0
10008983	Mr. Alan Turner	1973	Phd	Married	29603.00	1	0	Cork	Ireland	11/8/2017	...	8	2	3	9	0	0	0	0	0	0
10008985	Mr. Sean Quinn	1975	Phd	Married	83350.00	0	1	Killarney	Ireland	9/10/2018	...	12	6	10	6	0	0	0	0	0	0
10008993	Mr. Owen Wilson	1979	Master	Single	48647.00	2	1	Killarney	Ireland	3/21/2019	...	7	3	3	4	0	0	0	0	0	0
10009000	Mr. Jake Abraham	1973	Phd	Married	72674.00	0	1	Killarney	Ireland	4/4/2019	...	8	2	4	5	0	0	0	0	0	0
10009003	Mrs. Karen Welch	1988	Phd	Single	34809.00	1	0	Cork	Ireland	8/24/2018	...	6	3	2	6	0	0	0	0	0	0
10009009	Mr. Jake Langdon	1970	Phd	Married	81567.00	1	1	Kinsale	Ireland	4/27/2019	...	8	2	3	5	0	0	0	0	0	0
10009018	Mr. Stephen White	1974	Graduation	Married	144765.15	0	1	Cork	Ireland	6/27/2018	...	1	0	0	1	0	0	0	0	0	0
10009077	Mr. Richard Baker	1965	Graduation	Married	51644.00	1	1	Kinsale	Ireland	8/10/2018	...	7	3	2	5	1	0	0	0	0	0
10009079	Mrs. Julia Murray	1985	Phd	Married	51849.00	1	0	Cork	Ireland	3/3/2019	...	7	2	3	7	0	0	0	0	0	0
10009084	Mr. Christian Rampling	1993	Master	Married	54263.00	1	0	Cork	Ireland	6/29/2018	...	7	2	2	7	0	0	0	0	0	0
10009148	Mr. Dominic Mackay	1970	Phd	Married	35541.00	1	1	Cork	Ireland	12/18/2017	...	7	2	2	8	0	0	0	0	0	0
10009156	Mr. Michael Underwood	1977	Phd	Married	103359.00	0	1	Killarney	Ireland	8/29/2018	...	14	9	7	4	1	0	1	1	0	0
10009157	Mrs. Amelia Wright	1968	Phd	Married	71035.00	0	1	Killarney	Ireland	12/1/2018	...	11	4	4	6	0	0	0	0	0	0
10009163	Mr. Boris MacLeod	1953	Phd	Widow	78640.00	0	1	Kinsale	Ireland	4/6/2019	...	11	6	3	4	0	0	0	0	0	0
10009164	Mr. Robert Terry	1972	Graduation	Widow	40997.00	1	1	Cork	Ireland	10/9/2018	...	7	2	3	3	0	0	0	0	0	0
10009168	Mrs. Megan Watson	1988	Graduation	Single	18767.00	1	0	Cork	Ireland	2/28/2019	...	7	2	2	9	0	0	0	0	0	0
10009176	Miss Bernadette Howard	1948	Phd	Married	92087.00	0	0	Cork	Ireland	4/1/2019	...	13	4	12	3	0	1	1	0	0	0
10009198	Miss Theresa Payne	1974	Phd	Divorced	42046.00	1	0	Killarney	Ireland	9/27/2018	...	7	3	2	7	1	0	0	0	0	0
10009223	Miss Joanne Hardacre	1975	Master	Married	73011.00	0	1	Cork	Ireland	5/9/2019	...	7	2	3	4	0	0	0	0	0	0
10009227	Mrs. Jane Peters	1957	Phd	Married	72696.00	0	1	Cork	Ireland	3/12/2019	...	9	3	5	5	0	0	0	0	0	0
10009236	Mr. Gavin Allan	1967	Phd	Married	79380.00	0	1	Killarney	Ireland	4/17/2019	...	13	5	5	6	0	0	0	0	0	0
10009251	Mr. Thomas Russell	1974	Phd	Married	24225.00	1	0	Killarney	Ireland	2/20/2018	...	7	2	2	8	0	0	0	0	0	0
10009259	Mrs. Dorothy Rees	1971	Phd	Single	49878.00	1	1	Cork	Ireland	3/26/2019	...	6	2	3	5	0	0	0	0	0	0
10009261	Mr. Harry Mitchell	1963	Phd	Divorced	56324.00	1	1	Kinsale	Ireland	12/12/2018	...	8	2	3	7	0	0	0	0	0	0
10009282	Mr. Edward King	1973	Phd	Divorced	35427.00	1	0	Killarney	Ireland	11/27/2018	...	8	2	4	7	0	0	0	0	0	0
10009289	Mrs. Nicola Berry	1971	Graduation	Married	71524.00	0	1	Killarney	Ireland	7/13/2018	...	8	3	7	9	0	0	0	0	0	0
10009302	Mr. Christian Murray	1969	Phd	Married	58191.00	0	1	Killarney	Ireland	10/24/2017	...	7	2	2	6	0	0	0	0	0	0
10009309	Mrs. Theresa Taylor	1982	Master	Single	36340.00	1	0	Killarney	Ireland	11/8/2018	...	7	2	3	7	0	0	0	0	0	0
10009312	Mrs. Dorothy Blake	1983	Graduation	Married	73626.00	1	0	Kinsale	Ireland	5/18/2018	...	11	4	5	6	0	0	0	0	0	0
10009324	Mr. Steven Metcalfe	1981	Phd	Married	74478.00	0	1	Killarney	Ireland	8/13/2018	...	14	6	9	7	1	0	0	0	1	0
10009356	Mr. Sam Dyer	1969	Graduation	Single	29250.00	0	0	Cork	Ireland	3/27/2019	...	7	2	3	6	0	0	0	0	0	0
10009360	Mr. Thomas Peake	1980	Phd	Married	50027.00	0	2	Killarney	Ireland	3/1/2019	...	6	2	3	4	0	0	0	0	0	0
10009367	Mr. Phil Bailey	1981	Phd	Divorced	5286.30	0	1	Kinsale	Ireland	2/4/2019	...	30	0	0	1	0	0	0	0	0	0
10009369	Mrs. Lily Terry	1991	Graduation	Single	23867.00	1	0	Killarney	Ireland	10/22/2018	...	7	2	2	7	0	0	0	0	0	0
10009384	Mr. Sam McLean	1970	Phd	Single	15459.00	0	1	Killarney	Ireland	10/9/2018	...	8	3	3	6	0	0	0	0	0	1
10009410	Mr. Stewart Walsh	1975	Phd	Married	29857.00	1	0	Cork	Ireland	1/25/2018	...	8	2	3	8	0	0	0	0	0	0
10009427	Mr. Christian Harris	1962	Phd	Single	128536.00	0	0	Killarney	Ireland	8/1/2017	...	13	12	11	3	0	1	1	1	0	0
10009437	Mr. Cameron Walsh	1956	Phd	Married	38996.00	1	1	Kinsale	Ireland	6/14/2018	...	7	2	3	8	0	0	0	0	0	0
10009439	Mrs. Lillian Ince	1972	Phd	Married	51547.00	1	1	Kinsale	Ireland	11/10/2018	...	8	2	4	7	0	0	0	0	0	0
10009470	Mrs. Dorothy Young	1982	Phd	Married	59469.00	1	1	Cork	Ireland	2/16/2019	...	12	3	4	8	0	0	0	0	0	0
10009503	Mr. Adrian Roberts	1979	Phd	Married	60205.00	1	1	Kinsale	Ireland	7/26/2018	...	11	4	3	8	0	0	0	0	0	0
10009507	Mr. Ryan Alsop	1977	Phd	Married	125265.00	0	0	Cork	Ireland	8/18/2017	...	12	12	13	3	1	0	1	1	0	0
10009511	Mr. Ian Quinn	1983	Master	Married	94004.00	0	0	Killarney	Ireland	10/1/2017	...	8	6	9	6	0	1	1	0	0	0
10009514	Mr. Tim Sutherland	1962	Phd	Widow	44591.00	1	1	Cork	Ireland	11/4/2017	...	7	2	2	8	0	0	0	0	0	0
10009517	Mr. Adrian Grant	1962	Phd	Widow	59401.00	0	1	Kinsale	Ireland	9/1/2018	...	10	3	5	7	0	0	0	0	0	0
10009525	Mrs. Tracey Marshall	1970	Phd	Married	44414.00	1	1	Cork	Ireland	2/4/2019	...	7	2	3	7	0	0	0	0	0	0
10009540	Mr. James Gibson	1963	Master	Divorced	76677.00	0	1	Kinsale	Ireland	2/2/2018	...	16	4	12	7	0	1	0	0	0	0
10009541	Mrs. Emily Butler	1954	Phd	Married	57431.00	0	1	Killarney	Ireland	7/25/2018	...	8	2	3	6	0	0	0	0	0	0
10009547	Mrs. Emily Johnston	1964	Phd	Married	47029.00	1	1	Cork	Ireland	6/9/2019	...	8	2	4	5	0	0	0	0	0	0
10009563	Mrs. Ruth Paterson	1988	Phd	Divorced	32933.00	1	0	Killarney	Ireland	3/25/2018	...	7	2	2	7	1	0	0	0	0	0
10009567	Mr. Eric Burgess	1954	Phd	Widow	4335.40	1	1	Kinsale	Ireland	9/8/2017	...	1	0	0	13	0	0	0	0	0	0
10009578	Miss Lillian Hodges	1975	Graduation	Divorced	53398.00	0	1	Kinsale	Ireland	10/17/2018	...	7	2	3	6	0	0	0	0	0	0
10009637	Mr. Neil Burgess	1959	Graduation	Married	55955.00	1	1	Cork	Ireland	11/24/2018	...	7	2	3	4	0	0	0	0	0	0
10009662	Mr. Joshua Hill	1977	Graduation	Married	68715.00	0	1	Cork	Ireland	12/10/2017	...	8	4	11	8	0	0	0	0	0	0
10009667	Miss Hannah Black	1971	Phd	Married	79507.00	0	1	Killarney	Ireland	12/16/2018	...	10	3	8	4	0	0	0	0	0	0
10009672	Miss Kylie Henderson	1966	Phd	Married	62497.00	0	1	Killarney	Ireland	6/7/2019	...	7	2	2	5	0	0	0	0	0	0
10009702	Mr. Frank Mackay	1991	Phd	Divorced	37902.00	1	0	Kinsale	Ireland	10/4/2017	...	7	2	2	8	0	0	0	0	0	0
10009708	Mr. Phil Gibson	1982	Phd	Married	54212.00	1	1	Killarney	Ireland	4/25/2019	...	8	3	3	3	0	0	0	0	0	0
10009760	Mr. Edward Sharp	1969	Graduation	Married	86552.00	0	0	Cork	Ireland	9/20/2018	...	10	6	9	3	0	0	0	1	0	0
10009791	Mr. Stephen McDonald	1990	Phd	Married	49545.00	1	0	Killarney	Ireland	12/28/2017	...	10	3	5	7	0	0	0	0	0	0
10009796	Mr. Warren Carr	1962	Phd	Married	55332.00	0	1	Cork	Ireland	8/8/2018	...	15	3	7	9	0	0	0	0	0	0
10009810	Mr. Anthony North	1978	Phd	Single	71869.00	1	1	Killarney	Ireland	4/22/2019	...	11	3	5	7	0	1	0	0	0	0
10009828	Mr. Neil Gill	1962	Phd	Married	85190.00	0	1	Killarney	Ireland	4/6/2019	...	15	3	7	6	0	0	0	0	0	0
10009840	Mr. Neil Simpson	1956	Phd	Married	62855.00	0	1	Kinsale	Ireland	1/7/2019	...	7	3	2	6	0	0	0	0	0	0
10009848	Miss Amelia Allan	1972	Master	Divorced	81443.00	0	1	Killarney	Ireland	2/12/2019	...	16	5	7	7	0	1	0	1	0	0
10009853	Mr. Tim Dickens	1974	Phd	Married	63740.00	1	1	Kinsale	Ireland	9/24/2018	...	8	2	3	7	0	0	0	0	0	0
10009861	Mr. Joe Hill	1961	Phd	Single	72667.00	0	1	Kinsale	Ireland	2/8/2019	...	8	4	11	2	0	0	0	0	0	0
10009863	Mr. Colin Metcalfe	1972	Master	Married	50862.00	1	1	Cork	Ireland	4/22/2018	...	8	2	3	7	0	0	0	0	0	0
10009884	Mr. Kevin Hart	1961	Phd	Single	43971.00	1	1	Kinsale	Ireland	3/8/2018	...	7	2	3	6	0	0	0	0	0	0
10009897	Mr. David Cornish	1983	Master	Married	38822.00	1	0	Kinsale	Ireland	7/12/2018	...	7	3	2	6	0	0	0	0	0	0
10009899	Mr. Keith Metcalfe	1974	Master	Divorced	53553.00	0	1	Kinsale	Ireland	2/23/2018	...	8	2	3	7	0	0	0	0	0	0
10009904	Mrs. Virginia Robertson	1976	Phd	Married	41405.00	0	1	Kinsale	Ireland	5/20/2018	...	12	6	3	9	1	0	0	0	0	0
10009914	Mr. Luke Young	1986	Phd	Married	44627.00	1	0	Cork	Ireland	10/13/2017	...	9	2	4	7	0	0	0	0	0	0
10009919	Miss Amy Allan	1975	Phd	Single	39004.00	1	1	Kinsale	Ireland	1/19/2018	...	8	3	3	8	0	0	0	0	0	0
10009954	Mr. Alan Metcalfe	1979	Phd	Married	41063.00	1	1	Cork	Ireland	6/8/2018	...	7	2	2	8	0	0	0	0	0	0
10009969	Mr. Tim Sharp	1997	Master	Married	71126.00	0	0	Killarney	Ireland	1/21/2018	...	8	5	5	8	0	1	0	0	0	0
10009989	Mr. John Greene	1983	Phd	Married	29994.00	1	0	Kinsale	Ireland	8/21/2018	...	6	3	2	6	0	0	0	0	0	0
10010007	Miss Joanne Blake	1974	Phd	Married	72732.00	0	1	Kinsale	Ireland	3/4/2019	...	11	3	4	7	0	0	0	0	0	0
10010021	Mr. Stephen Pullman	1970	Phd	Divorced	75433.00	0	1	Killarney	Ireland	5/6/2018	...	13	5	7	5	0	0	0	0	0	0
10010022	Mrs. Elizabeth Scott	1967	Phd	Married	59566.00	1	1	Killarney	Ireland	3/8/2019	...	7	2	3	5	0	0	0	0	0	0
10010027	Mr. Ian Watson	1961	Phd	Married	75669.00	0	1	Killarney	Ireland	8/14/2018	...	13	4	9	5	0	0	0	0	0	0
10010048	Miss Victoria Roberts	1981	Phd	Married	52560.00	0	1	Cork	Ireland	2/22/2019	...	7	2	3	2	0	0	0	0	0	0
10010049	Mrs. Jasmine Bailey	1966	Phd	Single	59387.00	0	1	Cork	Ireland	9/3/2018	...	7	2	2	7	0	0	0	0	0	0
10010053	Miss Yvonne Butler	1989	Phd	Married	12591.00	1	0	Kinsale	Ireland	8/25/2017	...	7	2	3	8	0	0	0	0	0	0
10010066	Mrs. Ava Knox	1964	Phd	Divorced	77227.00	0	1	Cork	Ireland	3/14/2019	...	7	3	2	2	1	0	0	0	0	0
10010077	Mr. Warren Wilkins	1973	Phd	Married	62710.00	1	1	Cork	Ireland	4/15/2018	...	11	3	4	8	0	0	0	0	0	0
10010078	Mrs. Kimberly Tucker	1969	Master	Married	105755.00	0	1	Killarney	Ireland	10/24/2017	...	16	6	6	6	0	0	0	1	0	0
10010090	Miss Sonia Hodges	1964	Phd	Married	NaN	0	1	Killarney	Ireland	2/15/2018	...	14	4	8	7	0	0	0	0	0	0
10010093	Mr. Phil Metcalfe	1961	Phd	Married	56307.00	1	1	Cork	Ireland	3/26/2019	...	7	2	3	5	0	0	0	0	0	0
10010112	Mr. Nathan Duncan	1970	Phd	Married	41539.00	0	1	Cork	Ireland	1/14/2019	...	8	2	4	4	0	0	0	0	0	0
10010120	Mr. David Bond	1992	Master	Single	33729.00	1	0	Cork	Ireland	6/14/2019	...	7	2	3	6	0	0	0	0	0	0
10010126	Mr. Jake Sanderson	1956	Master	Married	91292.00	0	1	Killarney	Ireland	3/30/2018	...	11	3	8	9	0	1	0	0	0	0
10010140	Mr. Carl Morgan	1967	Phd	Married	76095.00	0	1	Cork	Ireland	1/25/2019	...	13	7	13	5	1	0	0	0	0	0
10010141	Mr. Stephen Bower	1962	Phd	Divorced	35190.00	0	1	Kinsale	Ireland	3/23/2019	...	7	2	3	4	0	0	0	0	0	0
10010150	Mr. Dan Miller	1976	Phd	Married	67598.00	2	1	Killarney	Ireland	7/1/2018	...	8	3	2	6	0	0	0	0	0	0
10010164	Mr. Matt Piper	1960	Phd	Married	49240.00	0	1	Killarney	Ireland	5/18/2018	...	11	4	4	8	0	0	0	0	0	0
10010183	Mr. Austin Skinner	1958	Phd	Married	97289.00	0	1	Killarney	Ireland	12/16/2017	...	13	4	8	5	1	0	0	0	0	0
10010186	Mr. Jake Berry	1971	Phd	Married	50636.00	1	2	Killarney	Ireland	9/7/2017	...	8	2	4	7	0	0	0	0	0	0
10010208	Mr. Warren Burgess	1979	Phd	Married	51695.00	1	0	Kinsale	Ireland	6/25/2019	...	7	2	2	5	0	0	0	0	0	0
10010223	Mrs. Wanda Miller	1980	Graduation	Married	20185.00	1	1	Cork	Ireland	5/6/2019	...	7	2	2	7	0	0	0	0	0	0
10010224	Mr. Eric Carr	1964	Master	Married	58201.00	1	1	Kinsale	Ireland	8/3/2018	...	7	2	3	7	0	0	0	0	0	0
10010228	Mr. Dylan Bailey	1980	Phd	Divorced	57826.00	1	1	Kinsale	Ireland	12/12/2017	...	9	3	5	5	0	0	0	0	0	0
10010241	Miss Penelope Mackenzie	1966	Phd	Married	89651.00	0	2	Killarney	Ireland	12/11/2017	...	17	4	11	7	0	0	0	0	0	0
10010242	Mrs. Rebecca Peters	1969	Phd	Married	70973.00	0	1	Kinsale	Ireland	12/27/2017	...	14	4	5	8	0	0	0	0	0	0
10010272	Mrs. Claire Nolan	1986	Graduation	Married	22464.00	1	1	Kinsale	Ireland	6/24/2018	...	7	2	3	4	0	0	0	0	0	0
10010317	Mrs. Pippa Tucker	1966	Phd	Married	86078.00	0	1	Killarney	Ireland	3/27/2019	...	14	4	8	6	0	0	0	0	0	0
10010318	Mr. Dylan Ross	1957	Graduation	Married	61592.00	1	1	Killarney	Ireland	5/24/2019	...	7	3	2	5	1	0	0	0	0	0
10010325	Mr. David Rees	1975	Graduation	Married	23531.00	1	0	Cork	Ireland	11/13/2018	...	9	2	3	8	0	0	0	0	0	0
10010342	Mr. Stewart Mills	1990	Phd	Married	56795.00	1	0	Cork	Ireland	6/19/2019	...	7	3	2	6	1	0	0	0	0	0
10010362	Miss Diana Davies	1969	Phd	Married	64961.00	0	1	Kinsale	Ireland	1/12/2018	...	11	4	6	5	0	0	0	0	0	0
10010367	Miss Grace Wilson	1979	2n cycle	Single	23880.00	1	0	Cork	Ireland	11/27/2017	...	7	2	2	7	0	0	0	0	0	0
10010374	Miss Jasmine Graham	1957	Phd	Single	56738.00	1	1	Kinsale	Ireland	1/13/2018	...	8	2	3	4	0	0	0	0	0	0
10010391	Mr. Jason Allan	1957	Phd	Single	88445.00	0	1	Kinsale	Ireland	4/4/2018	...	15	5	5	5	0	0	0	0	0	0
10010392	Mr. Brian Thomson	1977	Phd	Single	81139.00	1	1	Kinsale	Ireland	10/24/2017	...	15	5	7	7	0	0	0	0	0	0
10010407	Mr. Carl Rees	1965	Graduation	Single	86257.00	0	0	Cork	Ireland	8/31/2017	...	12	6	6	4	0	0	0	0	0	0
10010417	Mrs. Abigail Smith	1991	Phd	Married	30621.00	1	0	Killarney	Ireland	2/7/2019	...	9	2	3	9	0	0	0	0	0	0
10010418	Mr. Kevin Walsh	1976	Phd	Married	103509.00	0	0	Killarney	Ireland	3/11/2018	...	10	4	6	2	0	1	1	0	0	0
10010425	Mr. Simon Hardacre	1977	Phd	Married	50024.00	1	0	Kinsale	Ireland	5/13/2018	...	7	2	3	8	0	0	0	0	0	0
10010442	Mr. Julian Cornish	1964	Phd	Married	54828.00	1	1	Killarney	Ireland	5/1/2018	...	7	2	3	6	0	0	0	0	0	0
10010462	Mr. Thomas Mitchell	1962	Master	Married	66441.00	0	1	Cork	Ireland	5/17/2019	...	9	3	3	4	0	0	0	0	0	0
10010465	Mr. Matt Terry	1981	Graduation	Single	57748.00	1	0	Kinsale	Ireland	2/7/2019	...	8	3	3	5	0	0	0	0	0	0
10010506	Miss Jane Wilkins	1975	Phd	Married	49695.00	1	1	Cork	Ireland	5/2/2018	...	10	3	2	9	1	0	0	0	0	0
10010530	Miss Melanie Buckland	1958	Graduation	Single	31761.00	1	1	Cork	Ireland	5/17/2018	...	8	2	3	7	0	0	0	0	0	0
10010535	Miss Deirdre MacLeod	1965	Master	Married	62688.00	1	1	Killarney	Ireland	4/1/2019	...	10	2	4	9	0	0	0	0	0	0
10010543	Mr. Oliver Graham	1976	Phd	Married	39076.00	2	1	Kinsale	Ireland	6/21/2019	...	8	2	3	5	0	0	0	0	0	0
10010549	Mr. Carl Ferguson	1965	Phd	Married	71722.00	1	1	Cork	Ireland	9/5/2017	...	15	3	7	7	0	0	0	0	0	0
10010560	Miss Anne Powell	1988	Phd	Single	41538.00	1	0	Cork	Ireland	12/14/2017	...	7	2	3	8	0	0	0	0	0	0
10010568	Mr. Ian Paterson	1974	Phd	Married	99858.00	0	1	Kinsale	Ireland	6/26/2019	...	15	4	8	6	0	0	0	0	0	0
10010614	Mr. Brian McDonald	1962	Master	Married	52354.00	0	1	Kinsale	Ireland	5/14/2018	...	7	2	2	7	0	0	0	0	0	0
10010622	Mr. Nathan Quinn	1971	Phd	Divorced	52366.00	1	0	Cork	Ireland	3/1/2019	...	7	2	2	6	1	0	0	0	0	0
10010623	Mr. Julian Bower	1976	Phd	Divorced	21288.00	1	0	Cork	Ireland	9/15/2018	...	8	2	3	8	0	0	0	0	0	0
10010627	Mrs. Megan Springer	1979	Phd	Married	55385.00	1	0	Killarney	Ireland	12/10/2017	...	14	3	7	8	0	0	0	0	0	0
10010631	Mr. Jonathan Metcalfe	1991	Phd	Married	34281.00	1	0	Killarney	Ireland	3/1/2019	...	9	3	2	8	1	0	0	0	0	0
10010638	Mr. Simon Gill	1967	Phd	Married	51585.00	1	1	Kinsale	Ireland	6/11/2019	...	8	2	4	6	1	0	0	0	0	0
10010666	Mr. Gavin Avery	1965	Phd	Married	45877.00	1	1	Cork	Ireland	4/3/2019	...	7	2	2	7	0	0	0	0	0	0
10010688	Mr. Benjamin Payne	1951	Phd	Single	50934.00	0	1	Cork	Ireland	3/30/2018	...	11	3	5	8	0	0	0	0	0	0
10010692	Mr. Sean Grant	1969	Phd	Married	56630.00	1	2	Killarney	Ireland	2/18/2019	...	8	3	4	5	0	0	0	0	0	0
10010731	Mr. Joe Kerr	1973	Phd	Married	57868.00	0	0	Kinsale	Ireland	7/27/2018	...	9	3	3	8	0	0	0	0	0	0
10010736	Mr. Joseph Pullman	1975	Phd	Divorced	28488.00	1	0	Killarney	Ireland	5/15/2018	...	8	3	2	9	1	0	0	0	0	0
10010744	Mr. Simon Clark	1990	Phd	Married	94537.00	0	0	Killarney	Ireland	1/15/2019	...	9	10	7	1	1	0	1	1	0	0
10010745	Mr. Christopher Mackenzie	1962	Graduation	Married	52795.00	1	1	Kinsale	Ireland	1/17/2018	...	7	2	4	7	0	0	0	0	0	0
10010753	Mr. James Edmunds	1972	Phd	Married	54975.00	1	2	Kinsale	Ireland	2/7/2018	...	7	2	2	6	0	0	0	0	0	0
10010770	Mrs. Tracey Morrison	1989	Phd	Married	53640.00	1	0	Kinsale	Ireland	12/4/2017	...	10	3	4	8	0	0	0	0	0	0
10010774	Mr. Nicholas Glover	1974	Phd	Married	99791.00	0	0	Kinsale	Ireland	11/10/2018	...	9	10	6	1	0	0	0	0	0	0
10010776	Mr. Jason Clarkson	1978	Phd	Single	42383.00	1	0	Killarney	Ireland	3/21/2019	...	9	2	4	5	0	0	0	0	0	1
10010797	Mr. Austin Tucker	1981	Phd	Married	61353.00	1	1	Cork	Ireland	4/14/2019	...	7	2	3	7	1	0	0	0	1	0
10010813	Mrs. Tracey Vaughan	1984	Master	Single	26882.00	1	0	Cork	Ireland	11/9/2018	...	7	2	2	8	0	0	0	0	0	0
10010817	Mr. Neil Arnold	1957	Master	Married	41581.00	1	1	Killarney	Ireland	9/5/2018	...	7	2	3	5	0	0	0	0	0	0
10010818	Mr. Joshua Jackson	1958	Phd	Widow	49076.00	1	1	Kinsale	Ireland	12/21/2017	...	7	2	2	6	0	0	0	0	0	0
10010825	Mr. Adrian Vaughan	1983	Graduation	Married	71344.00	0	0	Kinsale	Ireland	1/14/2019	...	8	7	11	2	0	0	0	0	0	0
10010830	Mr. Andrew Simpson	1983	Phd	Divorced	62852.00	1	1	Cork	Ireland	3/7/2019	...	8	3	2	7	0	0	0	0	0	0
10010838	Mr. Alexander Cameron	1983	Graduation	Married	66428.00	1	1	Kinsale	Ireland	6/19/2018	...	7	2	3	6	0	0	0	0	0	0
10010841	Mrs. Megan Sanderson	1980	Phd	Single	44759.00	1	1	Kinsale	Ireland	6/25/2019	...	9	2	4	8	0	0	0	0	0	0
10010846	Mr. Jason Rutherford	1962	Phd	Married	30936.00	0	0	Cork	Ireland	5/11/2018	...	7	2	3	4	0	0	0	0	0	0
10010851	Miss Theresa Lyman	1963	Graduation	Single	87154.00	0	1	Killarney	Ireland	3/7/2018	...	10	5	7	8	0	0	0	0	0	0
10010854	Miss Deirdre MacDonald	1983	Phd	Married	78544.00	0	1	Cork	Ireland	2/10/2019	...	12	4	9	5	0	1	0	0	1	0
10010872	Mr. Julian Allan	1956	Phd	Divorced	105196.00	0	0	Cork	Ireland	9/7/2018	...	16	7	4	4	0	1	1	1	0	0
10010875	Mr. Nicholas Campbell	1992	Graduation	Married	36082.00	1	0	Kinsale	Ireland	8/14/2018	...	7	3	2	9	1	0	0	0	0	0
10010880	Mr. Leonard Scott	1986	Phd	Married	101777.00	0	0	Kinsale	Ireland	10/15/2017	...	10	9	12	2	0	0	0	0	0	0
10010893	Mr. Colin Lee	1973	Phd	Married	52082.00	1	0	Killarney	Ireland	3/26/2019	...	7	2	3	4	1	0	0	0	0	0
10010912	Mr. Connor Terry	1970	Phd	Married	92663.00	0	2	Killarney	Ireland	3/6/2018	...	15	7	13	6	0	0	0	0	0	0
10010914	Mr. Stewart Clark	1962	Phd	Single	59908.00	0	1	Killarney	Ireland	1/19/2019	...	8	3	3	5	0	0	0	0	0	0
10010918	Mr. Oliver Greene	1968	Phd	Married	56391.00	1	1	Cork	Ireland	3/6/2018	...	9	2	3	9	0	0	0	0	0	0
10010922	Mr. Stephen Hemmings	1958	Phd	Married	60275.00	2	1	Kinsale	Ireland	11/11/2017	...	9	3	6	6	0	1	0	0	0	0
10010958	Miss Sue Graham	1978	Master	Single	95758.00	0	1	Kinsale	Ireland	8/29/2018	...	9	5	5	6	0	1	1	0	0	0
10010963	Mrs. Sally Sharp	1977	Phd	Married	110081.00	0	1	Killarney	Ireland	8/23/2017	...	14	8	6	4	0	0	0	1	0	0
10010964	Mr. Keith MacDonald	1981	Phd	Married	55001.00	1	0	Kinsale	Ireland	11/14/2018	...	8	2	3	7	0	0	0	0	0	0
10010965	Mrs. Jessica McLean	1969	Phd	Single	52719.00	1	1	Killarney	Ireland	9/12/2017	...	7	2	2	8	0	0	0	0	0	0
10010996	Miss Theresa Springer	1980	Master	Married	41200.00	1	0	Kinsale	Ireland	8/2/2018	...	7	2	2	8	0	0	0	0	0	0
10010998	Mr. Keith Mills	1989	Phd	Married	66058.00	2	0	Kinsale	Ireland	2/13/2018	...	11	3	8	7	0	0	0	0	0	0
10011036	Mr. Julian Hughes	1970	Phd	Divorced	90817.00	0	1	Kinsale	Ireland	11/3/2017	...	12	6	8	4	0	0	0	0	0	0
10011064	Mr. Nathan Bell	1983	Phd	Married	79364.00	0	1	Kinsale	Ireland	3/12/2018	...	11	4	11	9	0	0	0	0	0	0
10011067	Mr. Isaac Peake	1979	Phd	Married	66943.00	0	1	Killarney	Ireland	7/17/2018	...	12	8	4	6	0	0	0	0	0	0
10011068	Mr. Owen Taylor	1984	Graduation	Married	56334.00	2	0	Cork	Ireland	2/13/2019	...	7	2	3	5	0	0	0	0	0	0
10011069	Mr. Jason Kerr	1973	Phd	Single	74391.00	0	1	Kinsale	Ireland	1/16/2019	...	13	3	7	7	0	0	0	0	0	0
10011075	Mr. Julian Ogden	1971	Phd	Divorced	80605.00	0	1	Cork	Ireland	3/7/2019	...	14	3	8	6	0	0	0	0	0	0
10011085	Mr. Boris Piper	1960	Graduation	Married	31710.00	1	1	Kinsale	Ireland	7/17/2018	...	7	2	3	8	1	0	0	0	0	0
10011112	Miss Karen Churchill	1964	Phd	Married	63912.00	0	1	Kinsale	Ireland	10/13/2018	...	12	4	4	7	0	0	0	0	0	0
10011133	Mrs. Theresa Gibson	1977	Phd	Married	67488.00	0	1	Cork	Ireland	4/19/2019	...	10	2	4	7	0	0	0	0	0	0
10011157	Mr. Adrian Welch	1963	Phd	Divorced	76712.00	1	1	Cork	Ireland	4/27/2019	...	10	3	6	5	0	1	0	0	0	0
10011163	Mr. Robert Harris	1970	Phd	Married	54752.00	1	2	Kinsale	Ireland	12/1/2018	...	8	4	2	4	1	0	0	0	0	0
10011179	Mrs. Julia Clark	1974	Phd	Married	121065.00	0	0	Kinsale	Ireland	10/28/2018	...	13	9	4	2	0	1	1	1	0	0
10011181	Mr. Sean Kelly	1977	Phd	Married	38817.00	1	0	Cork	Ireland	1/24/2018	...	7	3	2	8	0	0	0	0	0	0
10011193	Mr. Simon Clarkson	1982	Phd	Married	46682.00	1	0	Kinsale	Ireland	4/14/2018	...	7	2	3	6	0	0	0	0	0	0
10011195	Mr. Gordon Payne	1961	Phd	Married	48616.00	0	0	Killarney	Ireland	11/5/2018	...	12	4	7	7	0	0	0	0	0	0
10011197	Miss Karen Fisher	1951	Phd	Married	50177.00	1	1	Killarney	Ireland	9/21/2017	...	7	2	3	9	0	0	0	0	0	0
10011220	Mrs. Una Anderson	1983	Phd	Divorced	113895.00	0	0	Killarney	Ireland	10/14/2017	...	10	9	10	2	0	0	0	0	0	0
10011260	Mrs. Joan Cameron	1982	Phd	Married	53086.00	1	0	Killarney	Ireland	12/18/2017	...	7	2	2	8	0	0	0	0	0	0
10011261	Mr. Eric Black	1975	Master	Married	45658.00	1	1	Cork	Ireland	10/3/2018	...	7	2	3	7	0	0	0	0	0	0
10011262	Mrs. Karen Hardacre	1963	Phd	Single	42164.00	1	1	Killarney	Ireland	12/18/2017	...	10	3	4	7	0	0	0	0	0	0
10011266	Mr. Edward Knox	1975	Phd	Married	32811.00	1	0	Killarney	Ireland	10/3/2017	...	8	3	3	8	0	0	0	0	0	0
10011271	Mr. Victor Wilkins	1980	Phd	Married	38838.00	1	0	Killarney	Ireland	5/2/2019	...	7	2	2	5	0	0	0	0	0	0
10011274	Mr. Christopher Hunter	1993	Phd	Married	79799.00	0	0	Kinsale	Ireland	7/9/2018	...	12	9	9	4	0	0	0	0	0	0
10011288	Mr. Richard Ball	1961	Phd	Married	80266.00	0	1	Kinsale	Ireland	1/3/2018	...	12	4	11	6	0	0	0	0	0	0
10011291	Mr. Kevin Russell	1977	Phd	Married	32819.00	1	0	Cork	Ireland	11/25/2018	...	7	2	2	5	0	0	0	0	0	0
10011292	Mr. Jake Metcalfe	1975	Phd	Married	70195.00	0	1	Kinsale	Ireland	5/23/2018	...	13	4	7	6	0	0	0	0	0	0
10011295	Mr. Richard MacDonald	1982	Phd	Divorced	31180.00	1	0	Cork	Ireland	1/12/2018	...	7	2	3	6	0	0	0	0	0	0
10011336	Mr. Leonard Ince	1956	Phd	Married	61640.00	0	1	Kinsale	Ireland	5/21/2019	...	7	3	3	4	0	0	0	0	0	0
10011361	Mr. Justin Robertson	1962	Phd	Divorced	94531.00	0	2	Kinsale	Ireland	7/13/2018	...	15	7	10	6	0	0	0	0	0	0
10011362	Mr. Justin Walker	1959	Phd	Married	8090.10	0	1	Kinsale	Ireland	12/31/2018	...	2	0	1	14	0	1	0	0	1	0
10011387	Mr. Andrew Poole	1977	Phd	Married	56984.00	0	1	Killarney	Ireland	4/29/2019	...	7	2	2	7	0	0	0	0	0	0
10011393	Mr. Jason Terry	1979	Phd	Married	26276.00	1	0	Kinsale	Ireland	3/26/2018	...	8	3	2	8	0	0	0	0	0	0
10011403	Mr. Sebastian Paige	1977	Phd	Married	49509.00	0	1	Cork	Ireland	5/4/2019	...	9	2	4	6	0	0	0	0	0	0
10011407	Mr. Jacob Harris	1966	Phd	Married	57806.00	1	1	Kinsale	Ireland	6/23/2019	...	10	3	4	7	0	0	0	0	0	0
10011411	Mr. Blake Wallace	1956	Phd	Single	82978.00	0	0	Killarney	Ireland	4/1/2019	...	14	7	10	4	0	0	0	0	0	0
10011440	Mr. Ryan Lewis	1991	Graduation	Married	51203.00	1	0	Kinsale	Ireland	9/12/2017	...	10	3	5	7	0	0	0	0	0	0
10011465	Miss Maria Bell	1974	Phd	Single	31862.00	0	0	Killarney	Ireland	1/1/2019	...	7	2	3	8	1	0	0	0	0	0
10011484	Mr. Kevin Nash	1992	Phd	Single	4044.90	1	0	Killarney	Ireland	11/10/2017	...	28	0	0	1	0	0	0	0	0	0
10011489	Mr. Tim Abraham	1982	Phd	Married	70695.00	1	1	Cork	Ireland	11/11/2018	...	7	2	2	4	0	0	0	0	0	0
10011506	Mr. Michael Sutherland	1988	Phd	Single	50207.00	1	0	Killarney	Ireland	1/9/2019	...	9	2	4	7	0	0	0	0	0	0
10011518	Miss Lillian Peters	1971	Phd	Married	58870.00	1	0	Killarney	Ireland	1/9/2019	...	7	2	2	6	0	0	0	0	0	0
10011544	Mr. Alan Lee	1957	Master	Married	95806.00	0	1	Kinsale	Ireland	1/17/2018	...	11	7	11	3	0	0	0	0	0	0
10011563	Mr. Adam Churchill	1965	Phd	Married	72621.00	1	1	Kinsale	Ireland	2/10/2019	...	7	2	2	5	0	0	0	0	0	0
10011566	Mrs. Fiona Butler	1978	Phd	Married	76992.00	0	1	Killarney	Ireland	11/13/2018	...	15	3	4	8	0	0	0	0	0	0
10011568	Miss Jessica Jackson	1986	Phd	Single	31547.00	1	0	Killarney	Ireland	12/5/2018	...	7	2	3	7	0	0	0	0	0	0
10011591	Mr. Joshua Bond	1976	Master	Married	60917.00	0	1	Cork	Ireland	1/26/2019	...	9	3	8	4	0	1	0	0	0	0
10011603	Mrs. Carol Sharp	1971	Phd	Single	47508.00	0	1	Killarney	Ireland	12/15/2018	...	7	2	3	5	0	0	0	0	0	0
10011671	Mrs. Sophie Hodges	1988	Phd	Single	24134.00	1	0	Killarney	Ireland	6/18/2018	...	7	2	2	6	0	0	0	0	0	0
10011676	Miss Elizabeth Black	1984	Phd	Single	53465.00	1	0	Killarney	Ireland	4/27/2019	...	8	2	3	6	0	0	0	0	0	0
10011692	Mr. Frank Ferguson	1973	Phd	Married	92377.00	0	1	Kinsale	Ireland	6/15/2019	...	13	5	13	5	0	0	0	0	0	0
10011695	Mr. Evan May	1983	Phd	Married	80073.00	0	1	Cork	Ireland	2/23/2019	...	13	4	5	7	0	1	0	0	0	0
10011699	Mr. Nathan Davies	1980	Phd	Married	44749.00	1	1	Killarney	Ireland	6/26/2019	...	8	2	4	4	0	0	0	0	0	0
10011712	Mrs. Amelia Vaughan	1957	Master	Married	56162.00	0	1	Cork	Ireland	7/16/2018	...	12	3	5	7	0	0	0	0	0	0
10011723	Mr. David Kerr	1978	Phd	Married	47721.00	1	1	Killarney	Ireland	5/7/2018	...	7	2	3	8	0	0	0	0	0	0
10011740	Mrs. Dorothy Jones	1959	Phd	Single	48184.00	1	1	Cork	Ireland	7/17/2018	...	9	3	4	7	0	1	0	0	0	0
10011741	Mr. Connor Jones	1963	Phd	Married	54112.00	2	1	Kinsale	Ireland	3/21/2019	...	8	2	4	4	0	0	0	0	0	0
10011756	Mr. Jake Russell	1981	Phd	Married	34361.00	1	0	Cork	Ireland	1/2/2019	...	7	2	2	7	0	0	0	0	0	0
10011757	Mr. Brian Anderson	1978	Master	Married	76150.00	1	1	Kinsale	Ireland	2/7/2018	...	10	3	5	7	0	0	0	0	0	0
10011761	Mr. Dylan Quinn	1968	Phd	Married	43662.00	1	1	Killarney	Ireland	6/30/2018	...	7	2	3	6	0	0	0	0	0	0
10011771	Mr. Paul Paterson	1965	Phd	Married	49467.00	2	2	Kinsale	Ireland	5/4/2018	...	7	2	2	7	0	0	0	0	0	0
10011785	Mr. Blake Manning	1962	Phd	Single	110254.00	0	0	Kinsale	Ireland	4/11/2018	...	12	11	10	3	0	1	1	1	1	0
10011807	Mrs. Angela Vaughan	1969	Phd	Married	45962.00	0	1	Killarney	Ireland	6/12/2019	...	9	3	2	6	0	0	0	0	0	0
10011811	Mr. Max Forsyth	1972	Phd	Married	NaN	0	1	Cork	Ireland	4/8/2018	...	7	2	3	7	0	0	0	0	0	0
10011816	Mr. Jonathan Johnston	1972	Phd	Married	67408.00	1	1	Cork	Ireland	3/6/2019	...	12	3	5	8	0	0	0	0	0	0
10011819	Miss Jan Pullman	1986	Phd	Married	35468.00	1	0	Cork	Ireland	10/14/2017	...	8	2	3	9	0	0	0	0	0	0
10011823	Mr. Alexander Berry	1985	Phd	Married	38438.00	1	0	Cork	Ireland	10/23/2018	...	9	3	2	8	0	0	0	0	0	0
10011831	Miss Jane Forsyth	1954	Phd	Divorced	60851.00	1	1	Killarney	Ireland	1/7/2018	...	7	2	3	6	0	0	0	0	0	0
10011842	Mr. Austin Jones	1955	Phd	Widow	40762.00	0	0	Kinsale	Ireland	11/29/2018	...	8	2	4	6	0	0	0	0	0	0
10011863	Mr. Gavin Pullman	1982	Phd	Married	66958.00	0	1	Killarney	Ireland	5/19/2018	...	12	3	5	8	0	0	0	0	0	0
10011865	Mr. Alan Vance	1979	Phd	Married	77696.00	0	1	Killarney	Ireland	3/29/2018	...	16	4	7	8	0	0	0	0	0	0
10011866	Mr. Jason Hughes	1969	Phd	Married	42381.00	0	1	Kinsale	Ireland	12/6/2017	...	8	2	3	7	0	0	0	0	0	0
10011868	Mr. Jack Sharp	1982	Phd	Married	106031.00	0	0	Cork	Ireland	4/30/2019	...	8	10	11	0	0	0	0	0	0	0
10011871	Mr. Colin Howard	1967	Graduation	Married	66324.00	1	1	Kinsale	Ireland	9/16/2018	...	7	2	2	4	0	0	0	0	0	0
10011885	Mr. Joshua Reid	1988	Phd	Married	37521.00	1	0	Cork	Ireland	4/30/2019	...	7	2	2	7	0	0	0	0	0	0
10011891	Mr. Christopher Wright	1980	2n cycle	Married	30989.00	1	0	Killarney	Ireland	1/5/2018	...	7	2	3	8	0	0	0	0	0	0
10011898	Mr. Kevin Bell	1976	Phd	Single	88452.00	0	1	Kinsale	Ireland	4/14/2019	...	14	5	6	6	0	0	0	0	0	0
10011899	Mr. Jack McLean	1959	Graduation	Married	57511.00	1	1	Cork	Ireland	9/2/2017	...	7	2	2	7	0	0	0	0	0	0
10011904	Mr. Nathan Parsons	1958	Phd	Married	113174.00	0	0	Kinsale	Ireland	2/3/2019	...	11	12	13	2	1	0	1	1	0	0
10011917	Mr. Adam Hodges	1979	Phd	Married	121562.00	0	0	Kinsale	Ireland	3/28/2018	...	10	5	7	3	1	0	1	1	1	0
10011921	Mr. John Bailey	1962	Phd	Single	NaN	1	1	Killarney	Ireland	4/9/2019	...	7	2	2	6	0	0	0	0	0	0
10011931	Mr. Warren Skinner	1947	Phd	Widow	103199.00	0	0	Killarney	Ireland	3/1/2019	...	9	11	5	1	0	0	0	0	0	0
10011934	Mr. Andrew Lyman	1987	Master	Married	173422.05	1	0	Cork	Ireland	10/5/2017	...	1	0	0	1	0	0	0	0	0	0
10011949	Mr. William Mitchell	1980	Phd	Married	164143.95	0	1	Killarney	Ireland	5/24/2019	...	1	0	0	0	0	0	0	0	0	0
10011957	Mr. Eric Vaughan	1981	Phd	Single	71382.00	1	1	Killarney	Ireland	1/31/2019	...	8	3	3	5	0	0	0	0	0	0
10011967	Mrs. Anna Graham	1979	Phd	Single	70040.00	0	1	Cork	Ireland	4/21/2018	...	11	3	6	5	0	0	0	0	0	0
10011990	Mr. Ryan Walker	1983	Graduation	Married	17865.00	1	0	Cork	Ireland	11/9/2018	...	8	2	3	8	0	0	0	0	0	1
10012001	Miss Elizabeth Welch	1981	Master	Single	39053.00	1	0	Kinsale	Ireland	5/29/2018	...	7	2	2	7	0	0	0	0	0	0
10012004	Miss Alison Fisher	1970	Phd	Married	59546.00	0	1	Kinsale	Ireland	11/7/2018	...	8	2	3	6	0	0	0	0	0	0
10012012	Mrs. Lillian Gray	1972	Phd	Married	64439.00	0	1	Kinsale	Ireland	11/16/2018	...	7	2	3	5	0	0	0	0	0	0
10012024	Mrs. Joanne Dyer	1982	Graduation	Married	22623.00	1	0	Kinsale	Ireland	10/29/2018	...	7	2	3	8	0	0	0	0	0	0
10012025	Mr. Tim Churchill	1997	Graduation	Single	89857.00	0	0	Kinsale	Ireland	4/29/2018	...	10	9	12	2	0	1	0	1	0	0
10012027	Mrs. Fiona Wallace	1981	Phd	Married	58148.00	1	0	Cork	Ireland	6/29/2018	...	7	2	2	6	0	0	0	0	0	0
10012043	Mr. Jason White	1965	Phd	Married	72498.00	0	2	Kinsale	Ireland	2/16/2018	...	14	5	5	7	0	0	0	0	0	0
10012064	Mr. Neil Stewart	1977	Phd	Married	30788.00	1	1	Cork	Ireland	5/29/2019	...	8	3	3	7	0	0	0	0	0	0
10012070	Mr. Trevor Miller	1976	Phd	Married	56804.00	1	1	Killarney	Ireland	3/9/2018	...	7	2	2	7	0	0	0	0	0	0
10012073	Miss Gabrielle Vaughan	1956	Graduation	Divorced	72455.00	0	1	Killarney	Ireland	4/2/2018	...	12	5	5	6	0	0	0	0	0	0
10012083	Mr. Benjamin Mitchell	1959	Phd	Married	86735.00	0	1	Kinsale	Ireland	3/8/2018	...	12	8	13	7	0	1	1	1	1	1
10012085	Mr. Nathan Hudson	1990	Phd	Married	3092.90	1	0	Cork	Ireland	6/24/2018	...	1	0	0	13	0	0	0	0	0	0
10012091	Mr. Adrian Martin	1964	Master	Widow	71514.00	0	1	Kinsale	Ireland	6/16/2018	...	9	3	8	4	0	0	0	0	0	0
10012096	Mr. Warren Buckland	1984	Phd	Single	26974.00	1	0	Killarney	Ireland	11/19/2017	...	7	2	2	8	0	0	0	0	0	1
10012133	Mrs. Jane Duncan	1959	Phd	Married	71578.00	1	1	Cork	Ireland	2/24/2018	...	9	3	4	6	0	0	0	0	0	0
10012139	Mr. Kevin Payne	1961	Phd	Married	81270.00	0	1	Cork	Ireland	12/1/2018	...	10	3	5	6	0	0	0	0	0	0
10012147	Mr. Stephen Campbell	1990	Phd	Married	45859.00	1	0	Cork	Ireland	11/16/2018	...	7	2	2	7	0	0	0	0	0	0
10012168	Mr. Harry Bond	1975	Phd	Divorced	73615.00	0	1	Killarney	Ireland	11/24/2017	...	10	4	11	8	0	1	0	0	1	0
10012179	Mr. Peter Ince	1986	Phd	Single	37883.00	1	0	Kinsale	Ireland	3/5/2019	...	9	3	3	7	0	0	0	0	0	0
10012184	Mr. Cameron Murray	1975	Phd	Married	50927.00	1	1	Cork	Ireland	4/19/2019	...	8	2	4	5	0	0	0	0	0	0
10012196	Mr. Sam Ross	1980	Master	Married	45783.00	2	1	Kinsale	Ireland	6/3/2018	...	8	2	3	6	0	0	0	0	0	0
10012202	Mr. Adam Roberts	1960	Phd	Married	52209.00	1	1	Kinsale	Ireland	2/22/2018	...	7	2	2	9	0	0	0	0	0	0
10012208	Mr. Jacob Lewis	1974	Phd	Married	30268.00	1	0	Kinsale	Ireland	9/6/2018	...	7	2	3	5	0	0	0	0	0	0
10012218	Mrs. Amelia James	1990	Phd	Married	30092.00	1	0	Kinsale	Ireland	4/4/2019	...	7	2	2	8	0	0	0	0	0	0
10012220	Mrs. Carol Fraser	1963	Graduation	Married	61185.00	0	1	Killarney	Ireland	7/3/2018	...	7	2	3	6	0	0	0	0	0	0
10012239	Mr. Matt Parr	1985	Phd	Divorced	34628.00	1	0	Kinsale	Ireland	11/8/2018	...	8	2	3	7	0	0	0	0	0	0
10012260	Mrs. Audrey Poole	1967	Phd	Widow	102420.00	0	0	Killarney	Ireland	1/27/2018	...	13	5	5	5	0	1	1	1	0	0
10012261	Mr. Jack Ross	1964	Graduation	Married	116740.00	0	0	Cork	Ireland	2/10/2018	...	11	9	7	3	0	0	1	1	0	0
10012277	Mr. Piers Gill	1990	Master	Single	170568.15	1	0	Cork	Ireland	12/5/2018	...	29	0	0	1	0	0	0	0	0	0
10012285	Mr. Luke Thomson	1970	Phd	Single	141835.65	1	1	Kinsale	Ireland	12/3/2018	...	31	0	0	0	0	0	0	0	0	0
10012319	Miss Ella Parsons	1952	Phd	Widow	39462.00	0	1	Killarney	Ireland	5/10/2018	...	7	2	2	8	0	0	0	0	0	0
10012322	Mr. Lucas MacDonald	1976	Phd	Married	52685.00	0	1	Killarney	Ireland	5/25/2018	...	8	4	3	6	1	0	0	0	0	0
10012327	Mrs. Ava Rees	1955	Phd	Married	50422.00	1	1	Cork	Ireland	6/23/2018	...	7	3	3	5	0	0	0	0	0	0
10012345	Mrs. Carolyn Arnold	1973	Phd	Divorced	36212.00	1	1	Kinsale	Ireland	3/30/2019	...	7	2	3	6	0	0	0	0	0	0
10012354	Mr. Liam Greene	1966	Phd	Single	53247.00	0	1	Cork	Ireland	5/28/2019	...	10	4	5	5	0	0	0	0	0	0
10012366	Mr. Colin Sharp	1987	Phd	Single	48450.00	1	0	Kinsale	Ireland	5/3/2018	...	14	5	9	7	1	0	0	0	0	0
10012367	Mr. Blake Greene	1973	Graduation	Married	47241.00	0	1	Kinsale	Ireland	4/25/2019	...	7	2	3	3	0	0	0	0	0	0
10012372	Mr. Robert Clarkson	1964	Phd	Married	47646.00	1	1	Kinsale	Ireland	2/22/2019	...	7	2	3	7	0	0	0	0	0	0
10012390	Mr. Dylan Jones	1971	Phd	Divorced	45958.00	2	1	Kinsale	Ireland	5/5/2019	...	7	3	4	4	0	0	0	0	0	0
10012396	Miss Zoe Rutherford	1985	Phd	Married	108459.00	0	0	Cork	Ireland	6/22/2019	...	12	7	9	1	0	0	1	0	0	0
10012399	Mr. Blake Watson	1954	Phd	Married	51520.00	0	1	Kinsale	Ireland	12/10/2017	...	7	2	3	5	0	0	0	0	0	0
10012400	Miss Heather Young	1971	Phd	Married	47296.00	0	1	Kinsale	Ireland	4/21/2018	...	10	3	5	7	0	0	0	0	0	0
10012409	Mr. Justin Jackson	1982	Master	Married	25057.00	1	0	Killarney	Ireland	1/13/2018	...	8	2	4	8	0	0	0	0	0	0
10012415	Mr. Luke Lewis	1978	Phd	Married	68957.00	1	2	Cork	Ireland	7/27/2018	...	7	2	3	6	0	0	0	0	0	0
10012421	Miss Sonia Springer	1978	Phd	Married	63620.00	0	1	Cork	Ireland	4/10/2018	...	16	4	10	8	0	0	0	0	1	0
10012428	Mr. Boris Morgan	1955	Phd	Widow	81956.00	0	1	Killarney	Ireland	12/10/2017	...	11	4	10	5	0	0	0	0	0	0
10012434	Mr. Matt Ross	1971	Phd	Single	47524.00	1	1	Kinsale	Ireland	7/30/2018	...	7	2	2	8	0	0	0	0	0	0
10012439	Mr. Eric Rees	1982	Phd	Married	38067.00	1	0	Kinsale	Ireland	6/18/2018	...	7	2	2	9	0	0	0	0	0	0
10012440	Mr. Richard Buckland	1974	Phd	Married	97046.00	0	1	Killarney	Ireland	1/6/2018	...	14	6	9	5	0	1	0	0	0	0
10012455	Mr. Sebastian Gill	1973	Master	Single	73709.00	0	1	Killarney	Ireland	6/6/2018	...	17	3	7	9	0	1	0	0	0	0
10012478	Mr. Jason Metcalfe	1976	Phd	Married	94954.00	1	1	Killarney	Ireland	2/24/2018	...	9	4	8	8	1	0	0	0	1	0
10012511	Mr. Anthony Ogden	1964	Phd	Divorced	86891.00	0	0	Cork	Ireland	1/15/2019	...	16	6	13	6	1	0	1	1	1	0
10012546	Mr. Julian Poole	1986	Graduation	Single	33998.00	1	1	Killarney	Ireland	6/17/2018	...	7	2	2	7	0	0	0	0	0	0
10012572	Mr. Owen Reid	1982	Phd	Married	42368.00	1	0	Kinsale	Ireland	4/1/2019	...	7	2	3	6	0	0	0	0	0	0
10012579	Mr. Nicholas Blake	1969	Master	Married	66688.00	1	1	Killarney	Ireland	6/11/2019	...	6	2	3	3	0	0	0	0	0	0
10012604	Mr. Evan Wilkins	1957	Phd	Single	76559.00	0	1	Cork	Ireland	6/26/2018	...	11	3	6	7	0	1	0	0	0	0
10012608	Mr. Alan Mills	1978	Master	Widow	31411.00	0	1	Killarney	Ireland	8/9/2017	...	7	3	4	6	0	0	0	0	0	0
10012619	Mr. Simon Piper	1971	Phd	Single	40150.00	1	0	Kinsale	Ireland	10/12/2017	...	8	2	4	8	0	0	0	0	0	0
10012626	Mr. Joseph Metcalfe	1983	Phd	Married	31520.00	1	0	Kinsale	Ireland	7/30/2018	...	8	2	3	8	0	0	0	0	0	0
10012627	Mr. Lucas Gill	1977	Phd	Married	49419.00	1	1	Cork	Ireland	5/25/2018	...	7	2	3	7	0	0	0	0	0	0
10012635	Mr. Austin Hunter	1958	Graduation	Married	56910.00	1	1	Cork	Ireland	11/2/2018	...	8	2	4	5	0	0	0	0	0	0
10012644	Mr. Thomas Scott	1953	Phd	Married	65423.00	0	1	Cork	Ireland	3/14/2018	...	12	6	6	6	0	0	0	0	0	0
10012665	Mrs. Angela Robertson	1975	Phd	Married	53327.00	1	1	Kinsale	Ireland	5/23/2019	...	7	2	2	6	0	0	0	0	0	0
10012673	Mr. Evan Smith	1958	Phd	Single	63453.00	0	1	Killarney	Ireland	12/17/2018	...	10	3	6	6	1	0	0	1	1	0
10012689	Mr. Cameron Walker	1979	Phd	Married	71082.00	0	0	Cork	Ireland	8/10/2018	...	11	3	5	7	0	0	0	0	0	0
10012695	Miss Amelia Wilkins	1961	Master	Married	58795.00	1	1	Kinsale	Ireland	1/30/2019	...	7	2	2	6	0	0	0	0	0	0
10012699	Mrs. Emma Cameron	1977	Phd	Married	79003.00	0	1	Kinsale	Ireland	7/31/2017	...	13	6	11	5	0	0	0	0	0	0
10012727	Mr. Eric Hart	1967	Graduation	Married	35101.00	0	0	Killarney	Ireland	9/5/2018	...	6	2	3	3	0	0	0	0	0	1
10012759	Mr. Carl Ross	1965	Phd	Single	69221.00	0	1	Cork	Ireland	2/14/2019	...	11	4	4	7	0	0	0	0	0	0
10012760	Miss Jennifer Black	1975	Phd	Married	49954.00	1	1	Cork	Ireland	9/20/2018	...	7	2	2	5	1	0	0	0	0	0
10012796	Mr. Christian King	1979	Phd	Widow	106341.00	0	0	Killarney	Ireland	11/17/2018	...	8	11	10	1	0	0	0	0	0	0
10012804	Mr. Eric North	1956	Phd	Married	67649.00	0	1	Killarney	Ireland	5/12/2018	...	8	2	4	6	0	1	0	0	0	0
10012859	Miss Sue Anderson	1970	Phd	Married	61976.00	1	1	Killarney	Ireland	12/16/2018	...	9	2	3	8	1	0	0	0	0	0
10012864	Miss Rachel Oliver	1978	Phd	Married	37199.00	1	1	Cork	Ireland	8/21/2018	...	9	2	3	7	0	0	0	0	0	0
10012874	Miss Lisa Chapman	1977	Phd	Single	71331.00	0	1	Kinsale	Ireland	1/7/2019	...	11	4	3	7	1	0	0	0	0	0
10012896	Mr. Jason Lambert	1961	Phd	Married	50511.00	1	1	Cork	Ireland	12/29/2018	...	8	3	3	6	0	0	0	0	0	0
10012911	Mr. Austin Bailey	1979	Graduation	Married	28473.00	1	0	Kinsale	Ireland	8/21/2018	...	8	3	2	6	1	0	0	0	0	0
10012919	Mrs. Grace Davidson	1962	Phd	Married	50438.00	0	1	Kinsale	Ireland	4/26/2018	...	7	2	3	5	0	0	0	0	0	0
10012968	Mr. Sam Rees	1985	Phd	Married	36176.00	1	0	Kinsale	Ireland	11/15/2017	...	7	3	2	7	0	0	0	0	0	0
10012986	Mr. Colin Gibson	1950	Phd	Married	64614.00	0	0	Cork	Ireland	12/1/2018	...	9	4	5	5	0	0	0	0	0	0
10012990	Mr. Dylan Hill	1959	Graduation	Married	59725.00	0	2	Kinsale	Ireland	6/9/2019	...	8	2	3	6	0	0	0	0	0	0
10012995	Mr. John Johnston	1972	Phd	Married	102557.00	0	2	Killarney	Ireland	9/18/2017	...	8	4	10	6	0	0	0	0	0	0
10013001	Mr. Nicholas Parsons	1991	Phd	Married	187314.45	1	0	Cork	Ireland	1/29/2019	...	1	0	0	1	0	0	0	0	0	0
10013006	Miss Audrey Davies	1989	Phd	Married	28253.00	1	0	Killarney	Ireland	12/28/2018	...	8	3	3	5	0	0	0	0	0	0
10013023	Miss Vanessa Black	1962	Graduation	Married	86116.00	1	1	Killarney	Ireland	6/23/2019	...	7	3	4	3	0	0	0	0	0	0
10013024	Miss Joanne Howard	1981	Graduation	Married	50353.00	1	0	Cork	Ireland	2/8/2019	...	7	2	2	7	0	0	0	0	0	0
10013030	Mrs. Yvonne Slater	1979	Graduation	Married	61469.00	1	1	Killarney	Ireland	3/11/2019	...	7	2	2	7	0	0	0	0	0	0
10013039	Mrs. Angela Piper	1953	Phd	Widow	93674.00	0	1	Cork	Ireland	6/8/2018	...	14	6	7	5	0	0	0	0	0	0
10013052	Mr. Adam Dyer	1968	Phd	Married	6267.50	1	1	Kinsale	Ireland	6/27/2019	...	1	0	0	11	0	0	0	0	0	0
10013058	Mr. Alan Lambert	1977	Graduation	Married	74348.00	0	1	Killarney	Ireland	4/25/2019	...	10	3	3	6	0	1	0	0	0	0
10013064	Mr. Eric Davies	1967	Phd	Married	104990.00	0	1	Cork	Ireland	3/1/2018	...	8	7	7	6	0	0	0	0	0	0

806 rows × 28 columns

df_treated .fillna({
    'MntHouseKeeping': df_treated ['MntHouseKeeping'].median(),
    'MntWellness_&_Beauty': df_treated ['MntWellness_&_Beauty'].median(),
    'Income' : df_treated ['Income'].median(),
        }, inplace = True)
df_treated .isna().sum()

Name                         1
Year_Birth                   0
Education                    0
Marital_Status               0
Income                       0
Kidhome                      0
Teenhome                     0
Region                       0
Country                      0
Dt_Customer                  0
Recency                      0
MntGroceries                 0
MntStationery                0
MntHouseKeeping              0
MntWellness_&_Beauty         0
MntElectronics_&_Supplies    0
MntLimitedEdition            0
NumDealsPurchases            0
NumWebPurchases              0
NumCatalogPurchases          0
NumStorePurchases            0
NumWebVisitsMonth            0
AcceptedCmp1                 0
AcceptedCmp2                 0
AcceptedCmp3                 0
AcceptedCmp4                 0
AcceptedCmp5                 0
Complain                     0
dtype: int64

3.2 Data transformation

3.2.1 Create new variables

from datetime import date
df_treated ['Age'] = date.today().year - df_treated ['Year_Birth']

pd.set_option('display.max_rows', None)

df_treated ['Mnt'] = df_treated ['MntGroceries'] + df_treated ['MntStationery'] + df_treated ['MntHouseKeeping'] + df_treated ['MntWellness_&_Beauty'] + df_treated ['MntElectronics_&_Supplies'] + df_treated ['MntLimitedEdition']

df_treated ['freq'] =  df_treated ['NumWebPurchases'] + df_treated ['NumCatalogPurchases'] + df_treated ['NumStorePurchases']

df_treated ['MntFrq'] = df_treated ['Mnt']/df_treated ['freq']

df_treated ['Num_Cmp_acepted'] = df_treated ['AcceptedCmp1'] +  df_treated ['AcceptedCmp2'] + df_treated ['AcceptedCmp3'] + df_treated ['AcceptedCmp4'] + df_treated ['AcceptedCmp5']
df_treated ['Num_Cmp_acepted'].value_counts(ascending=True)

4      15
3      88
2     204
1     708
0    3794
Name: Num_Cmp_acepted, dtype: int64

#Check the median of costumers genders , to fill entry without name
df_aux_3 = df_treated .copy ()
df_aux_3['Gender'] = np.where(df_aux_3['Name'].str.startswith('Mr.' ), "Male", "Female")
df_aux_3['Gender'].value_counts(ascending=True)

Female    1578
Male      3231
Name: Gender, dtype: int64

df_treated [['Name']] =df_treated [['Name']] .fillna(value='Mr. John')

df_treated .describe(include=[object]).T

	count	unique	top	freq
Name	4809	4809	Mr. Christopher Short	1
Education	4809	4	Phd	2582
Marital_Status	4809	4	Married	3237
Region	4809	3	Kinsale	1630
Country	4809	3	Ireland	4805
Dt_Customer	4809	700	6/24/2018	18

df_treated ['Gender'] = np.where(df_treated ['Name'].str.startswith('Mr.' ), "Male", "Female")
df_treated ['Gender'].value_counts(ascending=True)

Female    1578
Male      3231
Name: Gender, dtype: int64

df_treated ['Dependents'] = df_treated ['Kidhome'] + df_treated ['Teenhome']

3.2.2 Drop non-relevant and redudant variables

del df_treated ['Year_Birth']
del df_treated ['Country']

3.2.3 Binning

df_treated ['age_bins'] = pd.cut(x=df_treated ['Age'], bins=[10, 20, 30, 40,50,60,70,80])

df_treated .head(n=5)

	Name	Education	Marital_Status	Income	Kidhome	Teenhome	Region	Dt_Customer	Recency	MntGroceries	...	AcceptedCmp5	Complain	Age	Mnt	freq	MntFrq	Num_Cmp_acepted	Gender	Dependents	age_bins
Card_ID
10003075	Mr. Adam Glover	Master	Married	90782.0	0	0	Cork	11/3/2018	66	622.0	...	0	0	40	1465.0	29	50.517241	1	Male	0	(30, 40]
10003076	Mr. Cameron McDonald	Phd	Single	113023.0	0	0	Kinsale	4/18/2019	6	1014.0	...	0	0	28	1818.0	21	86.571429	0	Male	0	(20, 30]
10003078	Mr. Keith Davidson	Phd	Single	93571.0	0	1	Cork	7/22/2018	10	639.0	...	0	0	39	1189.0	31	38.354839	0	Male	1	(30, 40]
10003079	Mr. Alexander Gill	Phd	Single	91852.0	0	1	Kinsale	2/3/2018	26	806.0	...	0	0	43	1580.0	35	45.142857	1	Male	1	(40, 50]
10003080	Mr. Neil Piper	Phd	Married	22386.0	1	1	Cork	10/31/2018	65	32.0	...	0	0	53	84.0	15	5.600000	1	Male	2	(50, 60]

5 rows × 34 columns

3.3 Check for incoherencies

nrecords_original = np.shape(df_treated)[0]

df_treated = df_treated[df_treated['Mnt'] < df_treated['Income']]
nrecords_after = np.shape(df_treated)[0]
print(nrecords_original)
print(nrecords_after)

4809
4809

df_treated = df_treated[((df_treated['Mnt'] > 0) & (df_treated['freq'] > 0))]
nrecords_after = np.shape(df_treated)[0]
print(nrecords_after)

4809

#Including only numeric columns in a DataFrame description.
df_treated.describe(include=[np.number]).T

	count	mean	std	min	25%	50%	75%	max
Income	4809.0	69516.435517	29170.551807	2553.200000	46507.000000	69172.500000	92507.000000	187314.45
Kidhome	4809.0	0.428156	0.537184	0.000000	0.000000	0.000000	1.000000	2.00
Teenhome	4809.0	0.476190	0.542212	0.000000	0.000000	0.000000	1.000000	2.00
Recency	4809.0	49.654814	29.035512	0.000000	25.000000	49.000000	75.000000	99.00
MntGroceries	4809.0	356.270514	400.739212	0.000000	28.000000	195.000000	596.000000	1799.00
MntStationery	4809.0	27.205822	40.360475	0.000000	2.000000	9.000000	34.000000	199.00
MntHouseKeeping	4809.0	184.109919	242.259019	0.000000	18.000000	69.000000	255.000000	1093.00
MntWellness_&_Beauty	4809.0	32.134227	47.502169	0.000000	3.000000	10.000000	40.000000	239.00
MntElectronics_&_Supplies	4809.0	35.158287	54.449422	0.000000	3.000000	11.000000	42.000000	315.00
MntLimitedEdition	4809.0	54.830131	69.057229	0.000000	10.000000	29.000000	70.000000	424.00
NumDealsPurchases	4809.0	2.025993	1.365561	0.000000	1.000000	2.000000	3.000000	6.00
NumWebPurchases	4809.0	10.048243	3.256595	1.000000	8.000000	9.000000	12.000000	31.00
NumCatalogPurchases	4809.0	4.532335	2.829575	0.000000	2.000000	4.000000	6.000000	13.00
NumStorePurchases	4809.0	5.679559	3.290725	0.000000	3.000000	4.000000	8.000000	13.00
NumWebVisitsMonth	4809.0	5.182366	2.544736	0.000000	3.000000	5.000000	7.000000	18.00
AcceptedCmp1	4809.0	0.078395	0.268820	0.000000	0.000000	0.000000	0.000000	1.00
AcceptedCmp2	4809.0	0.066334	0.248891	0.000000	0.000000	0.000000	0.000000	1.00
AcceptedCmp3	4809.0	0.080058	0.271412	0.000000	0.000000	0.000000	0.000000	1.00
AcceptedCmp4	4809.0	0.063839	0.244491	0.000000	0.000000	0.000000	0.000000	1.00
AcceptedCmp5	4809.0	0.010813	0.103433	0.000000	0.000000	0.000000	0.000000	1.00
Complain	4809.0	0.012685	0.111921	0.000000	0.000000	0.000000	0.000000	1.00
Age	4809.0	47.044500	11.915123	19.000000	38.000000	46.000000	56.000000	75.00
Mnt	4809.0	689.708900	687.150239	1.800000	84.000000	439.000000	1178.000000	3110.00
freq	4809.0	20.260137	7.451417	1.000000	14.000000	20.000000	26.000000	41.00
MntFrq	4809.0	29.264582	31.661759	0.818182	6.230769	21.857143	44.206897	445.30
Num_Cmp_acepted	4809.0	0.299439	0.664780	0.000000	0.000000	0.000000	0.000000	4.00
Dependents	4809.0	0.904346	0.735650	0.000000	0.000000	1.000000	1.000000	4.00

df_treated.head(n=5)

	Name	Education	Marital_Status	Income	Kidhome	Teenhome	Region	Dt_Customer	Recency	MntGroceries	...	AcceptedCmp5	Complain	Age	Mnt	freq	MntFrq	Num_Cmp_acepted	Gender	Dependents	age_bins
Card_ID
10003075	Mr. Adam Glover	Master	Married	90782.0	0	0	Cork	11/3/2018	66	622.0	...	0	0	40	1465.0	29	50.517241	1	Male	0	(30, 40]
10003076	Mr. Cameron McDonald	Phd	Single	113023.0	0	0	Kinsale	4/18/2019	6	1014.0	...	0	0	28	1818.0	21	86.571429	0	Male	0	(20, 30]
10003078	Mr. Keith Davidson	Phd	Single	93571.0	0	1	Cork	7/22/2018	10	639.0	...	0	0	39	1189.0	31	38.354839	0	Male	1	(30, 40]
10003079	Mr. Alexander Gill	Phd	Single	91852.0	0	1	Kinsale	2/3/2018	26	806.0	...	0	0	43	1580.0	35	45.142857	1	Male	1	(40, 50]
10003080	Mr. Neil Piper	Phd	Married	22386.0	1	1	Cork	10/31/2018	65	32.0	...	0	0	53	84.0	15	5.600000	1	Male	2	(50, 60]

5 rows × 34 columns

3.4 Create dummy Variables

df_treated ['Region'] = df_treated ['Region'].replace('Cork',0).replace('Killarney',1).replace('Kinsale',2)
df_treated ['Gender'] = df_treated ['Gender'].replace('Male',0).replace('Female',1)

df_treated ['Higher_Educ'] = np.where((df_treated ['Education']=='Graduation')|
                               (df_treated ['Education']=='Master')|
                               (df_treated ['Education']=='PhD'),1,0)

df_treated['Marital_Status'].value_counts()

Married     3237
Single      1020
Divorced     412
Widow        140
Name: Marital_Status, dtype: int64

df_treated ['Single_or_not'] = np.where((df_treated ['Marital_Status']=='Married'),1,0)
df_treated.head(n=5).T

Card_ID	10003075	10003076	10003078	10003079	10003080
Name	Mr. Adam Glover	Mr. Cameron McDonald	Mr. Keith Davidson	Mr. Alexander Gill	Mr. Neil Piper
Education	Master	Phd	Phd	Phd	Phd
Marital_Status	Married	Single	Single	Single	Married
Income	90782	113023	93571	91852	22386
Kidhome	0	0	0	0	1
Teenhome	0	0	1	1	1
Region	0	2	0	2	0
Dt_Customer	11/3/2018	4/18/2019	7/22/2018	2/3/2018	10/31/2018
Recency	66	6	10	26	65
MntGroceries	622	1014	639	806	32
MntStationery	70	15	88	56	1
MntHouseKeeping	678	643	185	350	29
MntWellness_&_Beauty	10	74	64	54	2
MntElectronics_&_Supplies	51	36	53	54	0
MntLimitedEdition	34	36	160	260	20
NumDealsPurchases	1	1	3	1	5
NumWebPurchases	12	9	13	15	9
NumCatalogPurchases	7	4	5	9	3
NumStorePurchases	10	8	13	11	3
NumWebVisitsMonth	4	1	5	6	9
AcceptedCmp1	0	0	0	0	1
AcceptedCmp2	1	0	0	0	0
AcceptedCmp3	0	0	0	0	0
AcceptedCmp4	0	0	0	1	0
AcceptedCmp5	0	0	0	0	0
Complain	0	0	0	0	0
Age	40	28	39	43	53
Mnt	1465	1818	1189	1580	84
freq	29	21	31	35	15
MntFrq	50.5172	86.5714	38.3548	45.1429	5.6
Num_Cmp_acepted	1	0	0	1	1
Gender	0	0	0	0	0
Dependents	0	0	1	1	2
age_bins	(30, 40]	(20, 30]	(30, 40]	(40, 50]	(50, 60]
Higher_Educ	1	0	0	0	0
Single_or_not	1	0	0	0	1

3.5 Normalize data

df_normalized = df_treated[['Age', 'Income', 'Mnt','Recency','MntGroceries','MntStationery','MntHouseKeeping',
                            'MntWellness_&_Beauty','MntElectronics_&_Supplies','MntLimitedEdition','NumDealsPurchases',
                            'NumWebPurchases','NumCatalogPurchases','NumWebVisitsMonth','NumStorePurchases','Dependents','freq' , 'AcceptedCmp1',
                            'AcceptedCmp2', 'AcceptedCmp3' ,'AcceptedCmp4' , 'AcceptedCmp5','Higher_Educ'
                           ]].copy()
df_normalized.head(n=5)

	Age	Income	Mnt	Recency	MntGroceries	MntStationery	MntHouseKeeping	MntWellness_&_Beauty	MntElectronics_&_Supplies	MntLimitedEdition	...	NumWebVisitsMonth	NumStorePurchases	Dependents	freq	AcceptedCmp1	AcceptedCmp2	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	Higher_Educ
Card_ID
10003075	40	90782.0	1465.0	66	622.0	70.0	678.0	10.0	51.0	34.0	...	4	10	0	29	0	1	0	0	0	1
10003076	28	113023.0	1818.0	6	1014.0	15.0	643.0	74.0	36.0	36.0	...	1	8	0	21	0	0	0	0	0	0
10003078	39	93571.0	1189.0	10	639.0	88.0	185.0	64.0	53.0	160.0	...	5	13	1	31	0	0	0	0	0	0
10003079	43	91852.0	1580.0	26	806.0	56.0	350.0	54.0	54.0	260.0	...	6	11	1	35	0	0	0	1	0	0
10003080	53	22386.0	84.0	65	32.0	1.0	29.0	2.0	0.0	20.0	...	9	3	2	15	1	0	0	0	0	0

5 rows × 23 columns

# Min-Max Normalization

df_normalized = df_normalized.apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)))
df_normalized.describe().T

	count	mean	std	min	25%	50%	75%	max
Age	4809.0	0.500795	0.212770	0.0	0.339286	0.482143	0.660714	1.0
Income	4809.0	0.362431	0.157882	0.0	0.237895	0.360570	0.486865	1.0
Mnt	4809.0	0.221321	0.221077	0.0	0.026446	0.140660	0.378418	1.0
Recency	4809.0	0.501564	0.293288	0.0	0.252525	0.494949	0.757576	1.0
MntGroceries	4809.0	0.198038	0.222757	0.0	0.015564	0.108394	0.331295	1.0
MntStationery	4809.0	0.136713	0.202816	0.0	0.010050	0.045226	0.170854	1.0
MntHouseKeeping	4809.0	0.168445	0.221646	0.0	0.016468	0.063129	0.233303	1.0
MntWellness_&_Beauty	4809.0	0.134453	0.198754	0.0	0.012552	0.041841	0.167364	1.0
MntElectronics_&_Supplies	4809.0	0.111614	0.172855	0.0	0.009524	0.034921	0.133333	1.0
MntLimitedEdition	4809.0	0.129316	0.162871	0.0	0.023585	0.068396	0.165094	1.0
NumDealsPurchases	4809.0	0.337665	0.227593	0.0	0.166667	0.333333	0.500000	1.0
NumWebPurchases	4809.0	0.301608	0.108553	0.0	0.233333	0.266667	0.366667	1.0
NumCatalogPurchases	4809.0	0.348641	0.217660	0.0	0.153846	0.307692	0.461538	1.0
NumWebVisitsMonth	4809.0	0.287909	0.141374	0.0	0.166667	0.277778	0.388889	1.0
NumStorePurchases	4809.0	0.436889	0.253133	0.0	0.230769	0.307692	0.615385	1.0
Dependents	4809.0	0.226087	0.183913	0.0	0.000000	0.250000	0.250000	1.0
freq	4809.0	0.481503	0.186285	0.0	0.325000	0.475000	0.625000	1.0
AcceptedCmp1	4809.0	0.078395	0.268820	0.0	0.000000	0.000000	0.000000	1.0
AcceptedCmp2	4809.0	0.066334	0.248891	0.0	0.000000	0.000000	0.000000	1.0
AcceptedCmp3	4809.0	0.080058	0.271412	0.0	0.000000	0.000000	0.000000	1.0
AcceptedCmp4	4809.0	0.063839	0.244491	0.0	0.000000	0.000000	0.000000	1.0
AcceptedCmp5	4809.0	0.010813	0.103433	0.0	0.000000	0.000000	0.000000	1.0
Higher_Educ	4809.0	0.454772	0.498002	0.0	0.000000	0.000000	1.000000	1.0

3.6 Power Transform

data_Groceries = df_normalized['MntGroceries'].to_list() 
data_Groceries = [x+1 for x in data_Groceries]
bcdata, lam = boxcox(data_Groceries)
df_normalized = df_normalized.assign( MntGroceries_BC = bcdata)

data_Stationery = df_normalized['MntStationery'].to_list()
data_Stationery = [x+1 for x in data_Stationery]
bcdata, lam = boxcox(data_Stationery)
df_normalized = df_normalized.assign( MntStationery_BC = bcdata)

data_HouseKeeping = df_normalized['MntHouseKeeping'].to_list()
data_HouseKeeping = [x+1 for x in data_HouseKeeping]
bcdata, lam = boxcox(data_HouseKeeping)
df_normalized = df_normalized.assign( MntHouseKeeping_BC = bcdata)

data_WellnessBeauty = df_normalized['MntWellness_&_Beauty'].to_list()
data_WellnessBeauty = [x+1 for x in data_WellnessBeauty]
bcdata, lam = boxcox(data_WellnessBeauty)
df_normalized = df_normalized.assign( MntWellnessBeauty_BC = bcdata)

data_Eletronics = df_normalized['MntElectronics_&_Supplies'].to_list()
data_Eletronics = [x+1 for x in data_Eletronics]
bcdata, lam = boxcox(data_Eletronics)
df_normalized = df_normalized.assign( MntEletronics_BC = bcdata)

data_LimitedEdition = df_normalized['MntLimitedEdition'].to_list()
data_LimitedEdition = [x+1 for x in data_LimitedEdition]
bcdata, lam = boxcox(data_LimitedEdition)
df_normalized = df_normalized.assign( MntLimitedEdition_BC = bcdata)

df_normalized.describe().T
df_normalized = df_normalized.apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)))
df_normalized.describe().T

	count	mean	std	min	25%	50%	75%	max
Age	4809.0	0.500795	0.212770	0.0	0.339286	0.482143	0.660714	1.0
Income	4809.0	0.362431	0.157882	0.0	0.237895	0.360570	0.486865	1.0
Mnt	4809.0	0.221321	0.221077	0.0	0.026446	0.140660	0.378418	1.0
Recency	4809.0	0.501564	0.293288	0.0	0.252525	0.494949	0.757576	1.0
MntGroceries	4809.0	0.198038	0.222757	0.0	0.015564	0.108394	0.331295	1.0
MntStationery	4809.0	0.136713	0.202816	0.0	0.010050	0.045226	0.170854	1.0
MntHouseKeeping	4809.0	0.168445	0.221646	0.0	0.016468	0.063129	0.233303	1.0
MntWellness_&_Beauty	4809.0	0.134453	0.198754	0.0	0.012552	0.041841	0.167364	1.0
MntElectronics_&_Supplies	4809.0	0.111614	0.172855	0.0	0.009524	0.034921	0.133333	1.0
MntLimitedEdition	4809.0	0.129316	0.162871	0.0	0.023585	0.068396	0.165094	1.0
NumDealsPurchases	4809.0	0.337665	0.227593	0.0	0.166667	0.333333	0.500000	1.0
NumWebPurchases	4809.0	0.301608	0.108553	0.0	0.233333	0.266667	0.366667	1.0
NumCatalogPurchases	4809.0	0.348641	0.217660	0.0	0.153846	0.307692	0.461538	1.0
NumWebVisitsMonth	4809.0	0.287909	0.141374	0.0	0.166667	0.277778	0.388889	1.0
NumStorePurchases	4809.0	0.436889	0.253133	0.0	0.230769	0.307692	0.615385	1.0
Dependents	4809.0	0.226087	0.183913	0.0	0.000000	0.250000	0.250000	1.0
freq	4809.0	0.481503	0.186285	0.0	0.325000	0.475000	0.625000	1.0
AcceptedCmp1	4809.0	0.078395	0.268820	0.0	0.000000	0.000000	0.000000	1.0
AcceptedCmp2	4809.0	0.066334	0.248891	0.0	0.000000	0.000000	0.000000	1.0
AcceptedCmp3	4809.0	0.080058	0.271412	0.0	0.000000	0.000000	0.000000	1.0
AcceptedCmp4	4809.0	0.063839	0.244491	0.0	0.000000	0.000000	0.000000	1.0
AcceptedCmp5	4809.0	0.010813	0.103433	0.0	0.000000	0.000000	0.000000	1.0
Higher_Educ	4809.0	0.454772	0.498002	0.0	0.000000	0.000000	1.000000	1.0
MntGroceries_BC	4809.0	0.384387	0.326027	0.0	0.058107	0.333565	0.696192	1.0
MntStationery_BC	4809.0	0.361306	0.338765	0.0	0.063965	0.253823	0.650370	1.0
MntHouseKeeping_BC	4809.0	0.391646	0.331690	0.0	0.086272	0.287724	0.693323	1.0
MntWellnessBeauty_BC	4809.0	0.365468	0.334998	0.0	0.080837	0.242151	0.651126	1.0
MntEletronics_BC	4809.0	0.358068	0.332602	0.0	0.073166	0.240629	0.634324	1.0
MntLimitedEdition_BC	4809.0	0.412137	0.294121	0.0	0.146401	0.362380	0.648046	1.0

fig, axes = plt.subplots(2,2, figsize = (15,10))

sns.histplot (ax = axes[0,0], data = df_normalized, x = 'MntStationery',kde=True).set_title('MntStationery after MixMax')
sns.histplot (ax = axes[0,1], data = df_normalized, x = 'MntStationery_BC',kde=True).set_title('MntStationery after BoxCox')
sns.histplot (ax = axes[1,0], data = df_normalized, x = 'MntElectronics_&_Supplies',kde=True).set_title('MntElectronics after MixMax')
sns.histplot (ax = axes[1,1], data = df_normalized, x = 'MntEletronics_BC',kde=True).set_title('MntElectronics after BoxCox')
plt.show()

3.7 Create flag variables for the transformed variables

var_list = ['MntGroceries', 'MntStationery', 'MntHouseKeeping' , 'MntWellness_&_Beauty' , 'MntElectronics_&_Supplies','MntLimitedEdition']

for category in var_list:
    
    data_list = df_treated[category].to_list() 
    
    for idx,y in enumerate (data_list):
        if y!=0:
            data_list[idx] = 1
        else :
            data_list[idx] = 0
    name= str(category + '_flag' )
    df_normalized[name] = data_list
df_normalized.head(n=10)

	Age	Income	Mnt	Recency	MntGroceries	MntStationery	MntHouseKeeping	MntWellness_&_Beauty	MntElectronics_&_Supplies	MntLimitedEdition	...	MntHouseKeeping_BC	MntWellnessBeauty_BC	MntEletronics_BC	MntLimitedEdition_BC	MntGroceries_flag	MntStationery_flag	MntHouseKeeping_flag	MntWellness_&_Beauty_flag	MntElectronics_&_Supplies_flag	MntLimitedEdition_flag
Card_ID
10003075	0.375000	0.477529	0.470755	0.666667	0.345748	0.351759	0.620311	0.041841	0.161905	0.080189	...	0.948346	0.242151	0.700952	0.408407	1	1	1	1	1	1
10003076	0.160714	0.597906	0.584325	0.060606	0.563646	0.075377	0.588289	0.309623	0.114286	0.084906	...	0.939702	0.843586	0.580766	0.425757	1	1	1	1	1	1
10003078	0.357143	0.492624	0.381957	0.101010	0.355197	0.442211	0.169259	0.267782	0.168254	0.377358	...	0.583150	0.803268	0.713852	0.892181	1	1	1	1	1	1
10003079	0.428571	0.483320	0.507754	0.262626	0.448027	0.281407	0.320220	0.225941	0.171429	0.613208	...	0.795004	0.751292	0.720069	0.968993	1	1	1	1	1	1
10003080	0.607143	0.107343	0.026446	0.656566	0.017788	0.005025	0.026532	0.008368	0.000000	0.047170	...	0.134755	0.054748	0.000000	0.268933	1	1	1	1	0	1
10003083	0.250000	0.578832	0.565665	0.757576	0.355197	0.633166	0.493138	0.635983	0.482540	0.358491	...	0.906227	0.972267	0.960659	0.880779	1	1	1	1	1	1
10003085	0.267857	0.513846	0.290586	0.939394	0.191217	0.618090	0.257091	0.347280	0.146032	0.066038	...	0.725702	0.872294	0.665783	0.352695	1	1	1	1	1	1
10003089	0.214286	0.258365	0.009073	0.313131	0.006115	0.000000	0.006404	0.016736	0.006349	0.014151	...	0.034624	0.106111	0.049466	0.090979	1	0	1	1	1	1
10003091	0.875000	0.094662	0.005855	0.373737	0.000000	0.020101	0.005489	0.020921	0.000000	0.011792	...	0.029764	0.130598	0.000000	0.076492	0	1	1	1	0	1
10003093	0.285714	0.127666	0.018081	0.525253	0.004447	0.030151	0.010064	0.041841	0.015873	0.042453	...	0.053786	0.242151	0.118606	0.246116	1	1	1	1	1	1

10 rows × 35 columns

3.8 Data Frame demographics

sns.scatterplot(x = 'Income', y= 'Mnt', data = df_treated , color='g',hue = "NumDealsPurchases" , palette="deep")

plt.title('Income vs Total amount spent vs Purchases using discount', color = 'k')
plt.legend(loc = 'upper right')
plt.xlabel("Income")
plt.ylabel("Total amount spent")
plt.show()

plt.figure(figsize=(10,6))
sns.scatterplot(x = 'Income', y= 'MntGroceries', data = df_treated ,hue = "MntHouseKeeping", palette = 'dark:salmon_r')

<matplotlib.axes._subplots.AxesSubplot at 0x2015153f9c8>

plt.figure(figsize=(10,6))
sns.scatterplot(x = 'Income', y= 'Mnt', data = df_treated ,hue = "MntLimitedEdition", palette = 'crest')

<matplotlib.axes._subplots.AxesSubplot at 0x20153eb6748>

plt.figure(figsize=(10,6))
sns.scatterplot(x = 'Income', y= 'Mnt', data = df_treated ,hue = 'Num_Cmp_acepted', palette = 'dark:salmon_r')

<matplotlib.axes._subplots.AxesSubplot at 0x201555de908>

sns.scatterplot(x = 'freq', y= 'Mnt', data = df_treated , color='green' )
plt.xlabel("Number of purchases")
plt.ylabel("Total amount spent")

Text(0, 0.5, 'Total amount spent')

sns.scatterplot(x = 'NumWebVisitsMonth', y= 'NumWebPurchases', data = df_treated , color='k',hue='Income' , palette= 'flare' )
plt.xlabel("NumWebVisitsMonth")
plt.ylabel("NumWebPurchases")

Text(0, 0.5, 'NumWebPurchases')

dfteste=df_treated .copy()
dfteste['AcceptedCmp1'] = dfteste['AcceptedCmp1'].replace(1,'Cmp1')
dfteste['AcceptedCmp2'] = dfteste['AcceptedCmp2'].replace(1,'Cmp2')
dfteste['AcceptedCmp3'] = dfteste['AcceptedCmp3'].replace(1,'Cmp3')
dfteste['AcceptedCmp4'] = dfteste['AcceptedCmp4'].replace(1,'Cmp4')
dfteste['AcceptedCmp5'] = dfteste['AcceptedCmp5'].replace(1,'Cmp5')

dfteste['AcceptedCmp1'] = dfteste['AcceptedCmp1'].replace(0,'x')
dfteste['AcceptedCmp2'] = dfteste['AcceptedCmp2'].replace(0,'x')
dfteste['AcceptedCmp3'] = dfteste['AcceptedCmp3'].replace(0,'x')
dfteste['AcceptedCmp4'] = dfteste['AcceptedCmp4'].replace(0,'x')
dfteste['AcceptedCmp5'] = dfteste['AcceptedCmp5'].replace(0,'x')

dfteste['TotalCmp'] = df_treated ['AcceptedCmp1']+df_treated ['AcceptedCmp2']+df_treated ['AcceptedCmp3']+df_treated ['AcceptedCmp4']+df_treated ['AcceptedCmp5']
dfteste['TotalCmp'] = np.where((dfteste.TotalCmp != 0),'x',dfteste.TotalCmp)
dfteste['TotalCmp'] = np.where((dfteste.TotalCmp != 'x'),'No Cmp',dfteste.TotalCmp)
#dfteste['TotalCmp'] = dfteste['TotalCmp'].replace(0,'No Cmp')
dfteste['TotalCmp']


fig, ax = plt.subplots()
ax.scatter(dfteste['Income'], dfteste['AcceptedCmp1'], color='purple' , alpha = 0.5)
ax.scatter(dfteste['Income'], dfteste['AcceptedCmp2'], color='blue',alpha = 0.5)
ax.scatter(dfteste['Income'], dfteste['AcceptedCmp3'], color='green',alpha = 0.5)
ax.scatter(dfteste['Income'], dfteste['AcceptedCmp4'], color='orange',alpha = 0.5)
ax.scatter(dfteste['Income'], dfteste['AcceptedCmp5'], color='red',alpha = 0.5)
ax.scatter(dfteste['Income'], dfteste['TotalCmp'], color='black',alpha = 0.5)


ax.set_xlabel("Income")
ax.set_ylabel("Number of Campaigns acepted")
plt.xticks(rotation=15)
ax.set_ylim([0.5,6.5])

plt.show()

df_treated['Num_Cmp_acepted'].value_counts()

0    3794
1     708
2     204
3      88
4      15
Name: Num_Cmp_acepted, dtype: int64

teen_max = df_treated ['Teenhome'].max()
teen_min = df_treated ['Teenhome'].min()

kid_max = df_treated ['Kidhome'].max()
kid_min = df_treated ['Kidhome'].min()


df_teens_kids = []
   
for k in range(kid_min, kid_max + 1):
    for t in range(teen_min, teen_max + 1):
        df_teens_kids.append(df_treated .loc[(df_treated ['Kidhome'] == k) & (df_treated ['Teenhome'] == t),'Teenhome'].count())

df_teens_kids = np.reshape(df_teens_kids, (kid_max + 1, teen_max + 1))

ax = plt.axes() 
sns.heatmap(df_teens_kids , linewidths=.5, annot=True, cmap="YlGnBu" ,fmt = "d" , ax = ax, cbar_kws={'label': 'Number of costumers'})
ax.set_title('Kids at home VS Teens at home ')
plt.xlabel('Teens at Home', fontsize = 10) 
plt.ylabel('Kids at home', fontsize = 10) 

plt.show()

Total=df_treated['Marital_Status'].value_counts().sum()


values = df_treated['Marital_Status'].value_counts().keys().tolist()
counts = df_treated['Marital_Status'].value_counts().tolist()

sns.barplot(x=values,y=counts, palette="deep")

ax = plt.gca()

position = 0
for value in counts:
        ax.text(position, value, '{:.1f}%'.format(value / Total * 100), color = 'black', ha = 'center', size = 'large' )
        
        position = position + 1

plt.grid(axis='y', alpha=0.5)
plt.xlabel('Marital Status')
plt.ylabel('Customer counts')
plt.show()

plt.figure(figsize=(10,6))
sns.scatterplot(x = 'Recency', y= 'Mnt', data = df_treated , color='green',marker="s" )
plt.xlabel("Number of days since last purchase")
plt.ylabel("Total amount spent")
plt.show()

Total=df_treated['Education'].value_counts().sum()


values = df_treated['Education'].value_counts().keys().tolist()
counts = df_treated['Education'].value_counts().tolist()

sns.barplot(x=values,y=counts, palette="deep")

ax = plt.gca()

position = 0
for value in counts:
        ax.text(position, value, '{:.1f}%'.format(value / Total * 100), color = 'black', ha = 'center',size = 'large')
        
        position = position + 1

plt.grid(axis='y', alpha=0.5)
plt.xlabel('Education')
plt.ylabel('Customer counts')
plt.show()

ax = sns.boxplot(x="Marital_Status", y="Mnt", data=df_treated ,palette="Set3")

plt.xlabel("Marital Status")
plt.ylabel("Total amount spent")

Text(0, 0.5, 'Total amount spent')

ax = sns.boxplot(x="Education", y="Mnt", data=df_treated ,palette="Set2")
plt.xlabel("Education")
plt.ylabel('Total amount spent')

Text(0, 0.5, 'Total amount spent')

ax = sns.stripplot(x = "Dependents", y="Mnt", data=df_treated  , jitter=0.3 ,linewidth=0.5, alpha=.5, s=5)
# ax = sns.swarmplot(x = "Dependents", y="Mnt", data=df_treated ,linewidth=0.5, alpha=.5, s=5)

plt.xlabel("Dependents")
plt.ylabel('Total amount spent')

Text(0, 0.5, 'Total amount spent')

plt.figure(figsize=(10,6))
box_plot = sns.boxplot(x="age_bins", y="Mnt", data=df_treated ,palette="crest")

medians = df_treated .groupby(['age_bins'])['Mnt'].median()
vertical_offset =df_treated ['Mnt'].median() * 0.1 


for xtick in box_plot.get_xticks():
    box_plot.text(xtick,medians[xtick] + vertical_offset,medians[xtick], 
            horizontalalignment='center',size='medium',color='w',weight='semibold')
plt.xlabel("Age group")
plt.ylabel("Total amount spent")
plt.show()



# stats = f_stats_boxplot(df_in['TotalPurchases'])
# axPurchases_00_00.text(stats[1], -.3, '{:.1f}'.format(stats[1]), color = 'red', ha = 'center')
# axPurchases_00_00.text(stats[3], .1, '{:.1f}'.format(stats[3]), color = 'black', ha = 'center')
# axPurchases_00_00.text(stats[5], -.3, '{:.1f}'.format(stats[5]), color = 'red', ha = 'center')

ax = sns.catplot(x="Dependents", y="Income", data=df_treated,kind="violin")
plt.xlabel("Dependents")
plt.ylabel('Income')
plt.show()

lista = df_normalized['MntStationery_BC']

fig, axes = plt.subplots(2,1, figsize = (5,6),gridspec_kw={'height_ratios': [1, 4]})
g1=sns.histplot(ax = axes[1], data = df_normalized, x = 'MntStationery_BC', color="pink", bins = 30)
g2=sns.boxplot(ax = axes[0], data = df_normalized, x = 'MntStationery_BC', color="red" )
g2.set(xlabel=None)
g2.set(title='MntStationery')
res = stats (lista)

axes[1].text(0, -5.3, 'Median = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
       color= 'k')
axes[1].text(0, -5.5, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
       color= 'k')
axes[1].text(0.4, -5.3, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
       color= 'k')
axes[1].text(0.4, -5.5, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
       color= 'k')

plt.xlabel("Amount spent")

plt.show()

# var_list= ['MntGroceries_BC','MntStationery_BC','MntHouseKeeping_BC','MntWellnessBeauty_BC','MntEletronics_BC','MntLimitedEdition_BC']

# for var in var_list:
    
#     lista = df_normalized[var]

#     fig, axes = plt.subplots(2,1, figsize = (3,7),gridspec_kw={'height_ratios': [1, 4]})
#     g1=sns.histplot(ax = axes[1], data = df_normalized, x = var, color="pink", bins = 30)
#     g2=sns.boxplot(ax = axes[0], data = df_normalized, x = var, color="red" )
#     g2.set(xlabel=None)
#     g2.set(title=var)
#     res = stats (lista)

#     axes[1].text(0, -5.3, 'Median = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
#            color= 'k')
#     axes[0].text(0, -5.6, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
#            color= 'k')
#     axes[1].text(0.7, -5.3, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
#            color= 'k')
#     axes[1].text(0.7, -5.6, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
#            color= 'k')

#     plt.xlabel("Amount spent")

#     plt.show()

  
# Creating dataset 
Cmp = ['Cmp1', 'Cmp2', 'Cmp3', 'Cmp4', 'Cmp5'] 
OffersbyCmp = pd.Series(df_treated[['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5']].sum(), name = 'Campaign')  

explode = (0.0, 0.0, 0.0, 0, 0.2) 
colors = ( "orange", "red", "deepskyblue", "green", "violet", "cadetblue","grey") 
wp = { 'linewidth' : 1, 'edgecolor' : "white" } 
  
fig, ax = plt.subplots(figsize =(10, 7)) 
wedges, texts, autotexts = ax.pie(OffersbyCmp, explode=explode, autopct='%1.1f%%', labels = Cmp, shadow = False, colors = colors, startangle = 80, wedgeprops = wp, textprops = dict(color ="black")) 

# legend 
ax.legend(wedges, Cmp, title ="Campaign", loc ="upper right", bbox_to_anchor =(0.7, 0, 0.5, 1)) 

ax.set_title("Campaings acceptance  ", fontsize=14, weight = "bold", color = "Black") 



plt.show() 

Mnt = ['MntGroceries','MntStationery','MntHouseKeeping','MntWellness_&_Beauty','MntElectronics_&_Supplies','MntLimitedEdition'] 
ProductUsage = pd.Series(df_treated[['MntGroceries', 'MntStationery', 'MntHouseKeeping', 'MntWellness_&_Beauty', 'MntElectronics_&_Supplies','MntLimitedEdition']].sum(), name = 'Product')  

explode = (0.0, 0.2, 0.0, 0.2, 0.2,0.2) 
  
colors = ( "orange", "red", "deepskyblue", "green", "violet", "cadetblue","grey") 
  
wp = { 'linewidth' : 1, 'edgecolor' : "white" } 
  
fig, ax = plt.subplots(figsize =(10, 7)) 
wedges, texts, autotexts = ax.pie(ProductUsage, explode=explode, autopct='%1.1f%%', labels = Mnt, shadow = False, colors = colors, startangle = 80, wedgeprops = wp, textprops = dict(color ="black")) 

ax.legend(wedges, Mnt, title ="Product category", loc ="lower left", bbox_to_anchor =(-0.4, 0, 0.5, 1)) 
  
ax.set_title("Total amount spend per category", fontsize=15, weight = "bold", color = "Black") 
  
# show plot 
plt.show() 

plt.figure(figsize=(8,4))
sns.scatterplot(x = 'Recency', y= 'Mnt', data = df_treated ,hue= 'Num_Cmp_acepted' ,palette ='flare')
plt.xlabel("Number of days since last purchase")
plt.ylabel("Total amount spent")
# plt.legend(loc='lower left', bbox_to_anchor=(0.6,0.5))
plt.legend(shadow=True, fancybox=True, ncol = 5, title="Number of campaigns accepted")
plt.ylim(top=4000)
plt.show()

4. Modelling

4.1 Clustering via K-means

4.1.1 Product usage segmentation

df_product_segmentation = df_normalized.copy()
# df_product_segmentation.describe().T

df_product_segmentation_k_means = df_product_segmentation.filter(items=['MntGroceries_BC', 'MntStationery_BC',
                'MntHouseKeeping_BC', 'MntWellnessBeauty_BC', 'MntEletronics_BC',
             'MntLimitedEdition_BC', 'MntGroceries_flag', 'MntStationery_flag' , 'MntHouseKeeping_flag' ,
            'MntWellness_&_Beauty_flag', 'MntElectronics_&_Supplies_flag' , 'MntLimitedEdition_flag']).copy()
df_product_segmentation_k_means.head(n = 5)

	MntGroceries_BC	MntStationery_BC	MntHouseKeeping_BC	MntWellnessBeauty_BC	MntEletronics_BC	MntLimitedEdition_BC	MntGroceries_flag	MntStationery_flag	MntHouseKeeping_flag	MntWellness_&_Beauty_flag	MntElectronics_&_Supplies_flag	MntLimitedEdition_flag
Card_ID
10003075	0.711100	0.869938	0.948346	0.242151	0.700952	0.408407	1	1	1	1	1	1
10003076	0.868892	0.382246	0.939702	0.843586	0.580766	0.425757	1	1	1	1	1	1
10003078	0.720462	0.918590	0.583150	0.803268	0.713852	0.892181	1	1	1	1	1	1
10003079	0.798530	0.810996	0.795004	0.751292	0.720069	0.968993	1	1	1	1	1	1
10003080	0.066082	0.032585	0.134755	0.054748	0.000000	0.268933	1	1	1	1	0	1

inertias_list = []

kmeans_kwargs = {
        "init": "random",
        "n_init": 40,
        "max_iter": 500,        
    }

for k in range (1,15):
    kmeans = KMeans(n_clusters = k, **kmeans_kwargs, precompute_distances = True , random_state= 100) 

    kmeans.fit(df_product_segmentation_k_means)

    inertias_list.append(kmeans.inertia_)

#plot number of clusters
plt.plot(range(1, 15), inertias_list)
plt.xlabel("Number of Clusters")
plt.ylabel("Inertia")
plt.xticks(np.arange(1, 15, 1))
plt.grid()
plt.show()

#Elbow location
kl = KneeLocator( range(1, 15), inertias_list, curve="convex", direction="decreasing")
kl.elbow

3

#After some iterations 6 cluster was established
n_clusters = 6
# Model training
model = KMeans(n_clusters = n_clusters, n_init = 40, max_iter = 2000 , random_state=100)
model.fit(df_product_segmentation_k_means)

KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=2000,
       n_clusters=6, n_init=40, n_jobs=None, precompute_distances='auto',
       random_state=100, tol=0.0001, verbose=0)

df_product_segmentation_k_means['product_clusters'] = model.labels_
df_treated['product_clusters'] = model.labels_
df_product_segmentation_k_means.head(n=5)
df_treated.head(n=5)

	Name	Education	Marital_Status	Income	Kidhome	Teenhome	Region	Dt_Customer	Recency	MntGroceries	...	Mnt	freq	MntFrq	Num_Cmp_acepted	Gender	Dependents	age_bins	Higher_Educ	Single_or_not	product_clusters
Card_ID
10003075	Mr. Adam Glover	Master	Married	90782.0	0	0	0	11/3/2018	66	622.0	...	1465.0	29	50.517241	1	0	0	(30, 40]	1	1	2
10003076	Mr. Cameron McDonald	Phd	Single	113023.0	0	0	2	4/18/2019	6	1014.0	...	1818.0	21	86.571429	0	0	0	(20, 30]	0	0	2
10003078	Mr. Keith Davidson	Phd	Single	93571.0	0	1	0	7/22/2018	10	639.0	...	1189.0	31	38.354839	0	0	1	(30, 40]	0	0	2
10003079	Mr. Alexander Gill	Phd	Single	91852.0	0	1	2	2/3/2018	26	806.0	...	1580.0	35	45.142857	1	0	1	(40, 50]	0	0	2
10003080	Mr. Neil Piper	Phd	Married	22386.0	1	1	0	10/31/2018	65	32.0	...	84.0	15	5.600000	1	0	2	(50, 60]	0	1	0

5 rows × 37 columns

variaveis = ['MntGroceries_BC', 'MntStationery_BC',
                'MntHouseKeeping_BC', 'MntWellnessBeauty_BC', 'MntEletronics_BC',
             'MntLimitedEdition_BC'] 

ax = sns.pairplot(df_product_segmentation_k_means, vars = variaveis,hue='product_clusters', palette = "tab10" , diag_kind = 'kde')

C:\Users\Tiago\anaconda3\lib\site-packages\seaborn\distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\Tiago\anaconda3\lib\site-packages\seaborn\distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)
C:\Users\Tiago\anaconda3\lib\site-packages\seaborn\distributions.py:306: UserWarning: Dataset has 0 variance; skipping density estimate.
  warnings.warn(msg, UserWarning)

fig, (ax1, ax2) =plt.subplots(1,2,figsize =(15, 5))

sns.scatterplot(x = 'Income', y= 'Mnt', data = df_treated ,hue = 'product_clusters', palette = 'tab10' , ax=ax1)
sns.scatterplot(x = 'freq', y= 'Mnt', data = df_treated , hue = 'product_clusters' , palette = 'tab10', ax=ax2)


ax1.set_xlabel('Income')
ax1.set_ylabel('Total amount spent')
ax2.set_xlabel("Number of purchases")
ax2.set_ylabel('Total amount spent')

Text(0, 0.5, 'Total amount spent')

# fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(17, 6), dpi=300)
# for i in range (0,6):  
#     size = df_product_segmentation_k_means[df_product_segmentation_k_means['cluster'] == i]['MntGroceries_BC'].size
#     sns.histplot(df_product_segmentation_k_means[df_product_segmentation_k_means['cluster'] == i]['MntGroceries_BC'],
#                  ax = axes[i], bins = 30 ).set_title('Cluster ' + str(i+1) +'  (N = ' + str(size) +')')
    
# plt.tight_layout()

# for i in range (1,6):
#     axes[i].set_ylabel('')  

# for i in range (0,6):
#         axes[i].set_xlim([0, 1])
# plt.show()

variaveis = ['MntGroceries', 'MntStationery',
                'MntHouseKeeping', 'MntWellness_&_Beauty', 'MntElectronics_&_Supplies',
             'MntLimitedEdition'] 
for var in variaveis:
    jet= plt.get_cmap('jet')
    colors = iter(jet(np.linspace(0,1,6)))
    fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(17, 6), dpi=300)
    for i in range (0,6):   
        size = df_treated[df_treated['product_clusters'] == i][var].size
        sns.histplot(df_treated[df_treated['product_clusters'] == i][var],
                     ax = axes[i], bins = 20 ,color=next(colors)).set_title('Cluster ' + str(i+1) +'  (N = ' + str(size) +')')
        
        res = stats (df_treated[df_treated['product_clusters'] == i][var])   
    
        axes[i].text(0.1+i*1.25, -0.15, 'Median = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
               color= 'k')
        axes[i].text(0.1+i*1.25, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
               color= 'k')
        axes[i].text(0.1+i*1.25, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
               color= 'k')
        axes[i].text(0.1+i*1.25, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
               color= 'k')          
    

    for i in range (1,6):
        axes[i].set_ylabel('')  

    plt.tight_layout()
    plt.show()

Total=df_treated['Income'].value_counts().sum()


fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(17, 6), dpi=300)
jet = plt.get_cmap('jet')
colors = iter(jet(np.linspace(0,1,6)))
for i in range (0,6):  
    
    size = df_treated[df_treated['product_clusters'] == i]['Income'].size
    percentage = round(size/Total*100,2)
    
    sns.histplot(df_treated[df_treated['product_clusters'] == i]['Income'],
                 ax = axes[i], bins = 20 ,color=next(colors)).set_title('Cluster ' + str(i+1) + '\n' +'  (N = ' + str(size) +') ' + str(percentage) + '%')
    res = stats (df_treated[df_treated['product_clusters'] == i]['Income'])   
    
    axes[i].text(0.1+i*1.22, -0.15, 'Median = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.22, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.22, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.22, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')


for i in range (1,6):
        axes[i].set_ylabel('')  

plt.tight_layout()
plt.show()

fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(17, 6), dpi=300)
jet = plt.get_cmap('jet')
colors = iter(jet(np.linspace(0,1,6)))

for i in range (0,6):  
    
    size = df_treated[df_treated['product_clusters'] == i]['Mnt'].size
    percentage = round(size/Total*100,2)
    sns.histplot(df_treated[df_treated['product_clusters'] == i]['Mnt'],
                 ax = axes[i], bins = 20 ,color=next(colors)).set_title('Cluster ' + str(i+1) + '\n' +'  (N = ' + str(size) +') ' + str(percentage) + '%')
    res = stats (df_treated[df_treated['product_clusters'] == i]['Mnt'])   
    
    axes[i].text(0.1+i*1.2, -0.15, 'Median = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    
for i in range (1,6):
    
        axes[i].set_ylabel('')  

plt.show()

fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(17, 6), dpi=300)
jet = plt.get_cmap('jet')
colors = iter(jet(np.linspace(0,1,6)))

for i in range (0,6):  
    
    size = df_treated[df_treated['product_clusters'] == i]['freq'].size
    percentage = round(size/Total*100,2)
    sns.histplot(df_treated[df_treated['product_clusters'] == i]['freq'],
                 ax = axes[i], bins = 20 ,color=next(colors)).set_title('Cluster ' + str(i+1) + '\n' +'  (N = ' + str(size) +') ' + str(percentage) + '%')
   
    res = stats (df_treated[df_treated['product_clusters'] == i]['freq'])   
    
    axes[i].text(0.1+i*1.2, -0.15, 'Median = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    
for i in range (1,6):
    
        axes[i].set_ylabel('')  

plt.show()

fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(20, 6))
for i in range (0,6):  
    values = df_treated[df_treated['product_clusters'] == i]['Education'].value_counts().keys().tolist()
    counts = df_treated[df_treated['product_clusters'] == i]['Education'].value_counts().tolist()
    
    size = df_treated[df_treated['product_clusters'] == i]['Education'].size
    percentage = round(size/Total*100,2)
    sns.barplot(x=values,y=counts,  ax = axes[i]).set_title('Cluster ' + str(i+1) + '\n' +'  (N = ' + str(size) +') ' + str(percentage) + '%')
    position = 0
    for value in counts:
            axes[i].text(position, value, '{:.2f}%'.format(value / Total * 100), color = 'black', ha = 'center', size = 'large' , va = 'bottom')

            position = position + 1
plt.tight_layout()

plt.show()

fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(20, 6))
for i in range (0,6):  
    values = df_treated[df_treated['product_clusters'] == i]['Marital_Status'].value_counts().keys().tolist()
    counts = df_treated[df_treated['product_clusters'] == i]['Marital_Status'].value_counts().tolist()
    size = df_treated[df_treated['product_clusters'] == i]['Marital_Status'].size
    percentage = round(size/Total*100,2)    
    sns.barplot(x=values,y=counts,  ax = axes[i] , palette = 'hls').set_title('Cluster ' + str(i+1) + '\n' +'  (N = ' + str(size) +') ' + str(percentage) + '%')
    position = 0
    for value in counts:
            axes[i].text(position, value, '{:.2f}%'.format(value / Total * 100), color = 'black', ha = 'center', size = 'large' , va = 'bottom')

            position = position + 1
plt.tight_layout()

plt.show()

# df_normalized.describe().T

fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(20, 6))
for i in range (0,6):  
    values = df_treated[df_treated['product_clusters'] == i]['age_bins'].value_counts().keys().tolist()
    counts = df_treated[df_treated['product_clusters'] == i]['age_bins'].value_counts().tolist()
    size = df_treated[df_treated['product_clusters'] == i]['age_bins'].size
    percentage = round(size/Total*100,2) 
    
    sns.barplot(x=values,y=counts,  ax = axes[i]).set_title('Cluster ' + str(i+1) + '\n' +'  (N = ' + str(size) +') ' + str(percentage) + '%')
    
    position = 0
    for value in counts:
            axes[i].text(position, value, '{:.2f}%'.format(value / Total * 100), color = 'black', ha = 'center', size = 'medium' , va = 'bottom')
            axes[i].set_xticklabels(axes[i].get_xticklabels(), rotation=30)
            position = position + 1
plt.tight_layout()

plt.show()

fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(17, 6), dpi=300)
jet = plt.get_cmap('jet')
colors = iter(jet(np.linspace(0,1,6)))

for i in range (0,6):  
    
    size = df_treated[df_treated['product_clusters'] == i]['NumWebPurchases'].size
    percentage = round(size/Total*100,2)
    sns.histplot(df_treated[df_treated['product_clusters'] == i]['NumWebPurchases'],
                 ax = axes[i], bins = 20 ,color=next(colors)).set_title('Cluster ' + str(i+1) + '\n' +'  (N = ' + str(size) +') ' + str(percentage) + '%')
   
    res = stats (df_treated[df_treated['product_clusters'] == i]['NumWebPurchases'])   
    
    axes[i].text(0.1+i*1.2, -0.15, 'Median = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    
for i in range (1,6):
    
        axes[i].set_ylabel('')  

plt.show()

fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(17, 6), dpi=300)
jet = plt.get_cmap('jet')
colors = iter(jet(np.linspace(0,1,6)))

for i in range (0,6):  
    
    size = df_treated[df_treated['product_clusters'] == i]['NumCatalogPurchases'].size
    percentage = round(size/Total*100,2)
    sns.histplot(df_treated[df_treated['product_clusters'] == i]['NumCatalogPurchases'],
                 ax = axes[i], bins = 20 ,color=next(colors)).set_title('Cluster ' + str(i+1) + '\n' +'  (N = ' + str(size) +') ' + str(percentage) + '%')
   
    res = stats (df_treated[df_treated['product_clusters'] == i]['NumCatalogPurchases'])   
    
    axes[i].text(0.1+i*1.2, -0.15, 'Median = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    
for i in range (1,6):
    
        axes[i].set_ylabel('')  

plt.show()

fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(17, 6), dpi=300)
jet = plt.get_cmap('jet')
colors = iter(jet(np.linspace(0,1,6)))

for i in range (0,6):  
    
    size = df_treated[df_treated['product_clusters'] == i]['NumStorePurchases'].size
    percentage = round(size/Total*100,2)
    sns.histplot(df_treated[df_treated['product_clusters'] == i]['NumStorePurchases'],
                 ax = axes[i], bins = 20 ,color=next(colors)).set_title('Cluster ' + str(i+1) + '\n' +'  (N = ' + str(size) +') ' + str(percentage) + '%')
   
    res = stats (df_treated[df_treated['product_clusters'] == i]['NumStorePurchases'])   
    
    axes[i].text(0.1+i*1.2, -0.15, 'Median = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    
for i in range (1,6):
    
        axes[i].set_ylabel('')  

plt.show()

fig, axes = plt.subplots(nrows=1, ncols=6, figsize=(17, 6), dpi=300)
jet = plt.get_cmap('jet')
colors = iter(jet(np.linspace(0,1,6)))

for i in range (0,6):  
    
    size = df_treated[df_treated['product_clusters'] == i]['NumDealsPurchases'].size
    percentage = round(size/Total*100,2)
    sns.histplot(df_treated[df_treated['product_clusters'] == i]['NumDealsPurchases'],
                 ax = axes[i], bins = 20 ,color=next(colors)).set_title('Cluster ' + str(i+1) + '\n' +'  (N = ' + str(size) +') ' + str(percentage) + '%')
   
    res = stats (df_treated[df_treated['product_clusters'] == i]['NumDealsPurchases'])   
    
    axes[i].text(0.1+i*1.2, -0.15, 'Median = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    
for i in range (1,6):
    
        axes[i].set_ylabel('')  

plt.show()

4.1.2 key Insights

After data cleaning our data frame reamined with a total of 4809 rows (clients). In the first segmentation (product usage), a number of 6 cluster was used. Two clusters contain more than 30 % of the costumers population, as the remained 4 clusters contain between 6 to 10 % of the costumers.

Cluster (N = 1727 ; 35.91 %) - High value costumers : It is the cluster with more costumers, and represents the costumers with higher income (median = 95076) that spends more money in the store (median =1294) , and regardless of the product category;

Cluster (N = 1600 ; 33.27 %) - Low budget costumers : Also represents a large amount of the costumers, and corresponds to the costumers with less income (median = 49523) and that on majority only spend money on essential products (Groceries and house keeping).

Cluster (N = 309 ; 6.43 %) - Non stationary and books :

Cluster (N = 393 ; 8.17 %) - Non wellness and beauty :

Cluster (N = 340 ; 7.07 %) - Non eletronics :

Cluster (N = 440 ; 9.15 %) - Ocasionally essential costumers : only buys products for the groceries and house keeping categories. Visit the store not very regularly (freq median = 14), the cluster that spends less in the store (median = 58). Differs from cluster (N = 1727 ; 35.91 %), eventough they have a higher income they spend less.

4.2.1 Costumer value segmentation (Recency vs Mnt vs Freq)

df_costumer_segmentation = df_normalized.copy()

df_costumer_segmentation_k_means = df_costumer_segmentation.filter(items=[ 'Recency',
                 'freq','Mnt']).copy()
df_costumer_segmentation_k_means.head(n = 5)

	Recency	freq	Mnt
Card_ID
10003075	0.666667	0.70	0.470755
10003076	0.060606	0.50	0.584325
10003078	0.101010	0.75	0.381957
10003079	0.262626	0.85	0.507754
10003080	0.656566	0.35	0.026446

inertias_list = []

kmeans_kwargs = {
        "init": "random",
        "n_init": 40,
        "max_iter": 500,
        "random_state": 100,
    }

for k in range (1,15):
    kmeans = KMeans(n_clusters = k, **kmeans_kwargs, precompute_distances = True)

    kmeans.fit(df_costumer_segmentation_k_means)

    inertias_list.append(kmeans.inertia_)

#plot number of clusters
plt.plot(range(1, 15), inertias_list)
plt.xlabel("Number of Clusters")
plt.ylabel("Inertia")
plt.xticks(np.arange(1, 15, 1))
plt.grid()
plt.show()

n_clusters = 4
# Model training
model = KMeans(n_clusters = n_clusters, n_init = 40, max_iter = 2000, random_state=100)
model.fit(df_costumer_segmentation_k_means)

KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=2000,
       n_clusters=4, n_init=40, n_jobs=None, precompute_distances='auto',
       random_state=100, tol=0.0001, verbose=0)

df_costumer_segmentation_k_means['value_cluster'] = model.labels_
df_treated['value_cluster'] = model.labels_
df_costumer_segmentation_k_means.head(n=5)

	Recency	freq	Mnt	value_cluster
Card_ID
10003075	0.666667	0.70	0.470755	1
10003076	0.060606	0.50	0.584325	2
10003078	0.101010	0.75	0.381957	2
10003079	0.262626	0.85	0.507754	2
10003080	0.656566	0.35	0.026446	3

variaveis = ['Recency',
                 'freq','Mnt'] 

ax = sns.pairplot(df_costumer_segmentation_k_means, vars = variaveis,hue='value_cluster', palette = "tab10" , diag_kind = 'kde')

fig, ax = plt.subplots()
ax = sns.scatterplot(x = 'Recency', y= 'Mnt', data = df_treated ,hue = 'value_cluster', palette = 'tab10')
plt.xlabel("Days since last purchase")
plt.ylabel("Total amount spent")


plt.legend(shadow=True, fancybox=True, ncol = 5, title="Cluster")
plt.ylim(top=4000)

for i in range (0,4):  
    size = df_treated[df_treated['value_cluster'] == i]['Recency'].size
    percentage = round(size/Total*100,2)  
    
    ax.text(1.05, 0.1+(i-0.9*i), 'Cluster:' + str(i) + '  '+ str(percentage) + ' %  ' + '(N=' + str(size) + ')', transform=ax.transAxes, fontsize=12)

fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(17, 6), dpi=300)
jet = plt.get_cmap('jet')
colors = iter(jet(np.linspace(0,1,4)))

for i in range (0,4):  
    
    size = df_treated[df_treated['value_cluster'] == i]['NumDealsPurchases'].size
    percentage = round(size/Total*100,2)
    sns.histplot(df_treated[df_treated['value_cluster'] == i]['NumDealsPurchases'],
                 ax = axes[i], bins = 20 ,color=next(colors)).set_title('Cluster ' + str(i+1) + '\n' +'  (N = ' + str(size) +') ' + str(percentage) + '%')
   
    res = stats2 (df_treated[df_treated['value_cluster'] == i]['NumDealsPurchases'])   
    
    axes[i].text(0.1+i*1.2, -0.15, 'Mean = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    
for i in range (1,4):
    
        axes[i].set_ylabel('')  

plt.show()

4.2.2 Key insights

• Cluster of recent casual buyers (28.61 %, N = 1376)
  Customers that have purchased recently, but don't spent a lot of money in the store.

• Cluster of non-recent Premium buyers (22.13 %, N = 1065)
  Customers with high frequency and total amount spend, however they haven't made purchases in the store recently.

• Cluster of recent Premium buyers(21.65 %, N = 1041)
  Cluster with the most valuable clients, the ones that buy frequently form the store and spend high amounts of money

• Cluster of non-recent casual buyers (27.61 %, N = 1328)
  Cluster with the clients that have not purchased recently, nor spent a lot of money in the store.

4.3.1 Costumer segmentation by campaign acceptance

df_costumer_segmentation = df_normalized.copy()
# df_costumer_segmentation.describe().T
# AcceptedCmp1

df_costumer_segmentation_k_means = df_costumer_segmentation.filter(items=['AcceptedCmp1',
                 'AcceptedCmp2','AcceptedCmp3','AcceptedCmp4','AcceptedCmp5','Mnt']).copy()
df_costumer_segmentation_k_means.head(n = 5)

	AcceptedCmp1	AcceptedCmp2	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	Mnt
Card_ID
10003075	0.0	1.0	0.0	0.0	0.0	0.470755
10003076	0.0	0.0	0.0	0.0	0.0	0.584325
10003078	0.0	0.0	0.0	0.0	0.0	0.381957
10003079	0.0	0.0	0.0	1.0	0.0	0.507754
10003080	1.0	0.0	0.0	0.0	0.0	0.026446

inertias_list = []

kmeans_kwargs = {
        "init": "random",
        "n_init": 40,
        "max_iter": 500,
        "random_state": 100,
    }

for k in range (1,15):
    kmeans = KMeans(n_clusters = k, **kmeans_kwargs, precompute_distances = True)

    kmeans.fit(df_costumer_segmentation_k_means)

    inertias_list.append(kmeans.inertia_)

#plot number of clusters
plt.plot(range(1, 15), inertias_list)
plt.xlabel("Number of Clusters")
plt.ylabel("Inertia")
plt.xticks(np.arange(1, 15, 1))
plt.grid()
plt.show()

n_clusters = 5
# Model training
model = KMeans(n_clusters = n_clusters, n_init = 40, max_iter = 2000, random_state=100)
model.fit(df_costumer_segmentation_k_means)

KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=2000,
       n_clusters=5, n_init=40, n_jobs=None, precompute_distances='auto',
       random_state=100, tol=0.0001, verbose=0)

df_costumer_segmentation_k_means['cluster'] = model.labels_
df_treated['cluster'] = model.labels_
df_costumer_segmentation_k_means.head(n=5)

	AcceptedCmp1	AcceptedCmp2	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	Mnt	cluster
Card_ID
10003075	0.0	1.0	0.0	0.0	0.0	0.470755	2
10003076	0.0	0.0	0.0	0.0	0.0	0.584325	0
10003078	0.0	0.0	0.0	0.0	0.0	0.381957	0
10003079	0.0	0.0	0.0	1.0	0.0	0.507754	1
10003080	1.0	0.0	0.0	0.0	0.0	0.026446	4

print('Amount of campaigns accepted ')
print(df_treated['Num_Cmp_acepted'].value_counts())

Amount of campaigns accepted 
0    3794
1     708
2     204
3      88
4      15
Name: Num_Cmp_acepted, dtype: int64

fig, ax = plt.subplots()
df_loc = []
df_loc = ( df_treated.loc[df_treated['AcceptedCmp1'] == 1, 'cluster'] )
df_loc = df_loc.value_counts().rename_axis('cluster').to_frame('Campaign 1')

df_loc2 = ( df_treated.loc[df_treated['AcceptedCmp2'] == 1, 'cluster'] )
df_loc2 = df_loc2.value_counts().rename_axis('cluster').to_frame('Campaign 2')

df_loc3 = ( df_treated.loc[df_treated['AcceptedCmp3'] == 1, 'cluster'] )
df_loc3 = df_loc3.value_counts().rename_axis('cluster').to_frame('Campaign 3')

df_loc4 = ( df_treated.loc[df_treated['AcceptedCmp4'] == 1, 'cluster'] )
df_loc4 = df_loc4.value_counts().rename_axis('cluster').to_frame('Campaign 4')

df_loc5 = ( df_treated.loc[df_treated['AcceptedCmp5'] == 1, 'cluster'] )

df_loc5 = df_loc5.value_counts().rename_axis('cluster').to_frame('Campaign 5')

df_loc6 = ( df_treated.loc[df_treated['Num_Cmp_acepted'] == 0, 'cluster'] )
df_loc6 = df_loc6.value_counts().rename_axis('cluster').to_frame('No campaign')

result = pd.concat([df_loc, df_loc2,df_loc3,df_loc4,df_loc5,df_loc6], axis=1)
result = result.fillna(0)

sns.heatmap(result, linewidths=.5, annot=True, cmap="YlGnBu_r" ,fmt='g')
som=pd.DataFrame(result.sum(), columns=['sum'])
ax.text(-0.8, -0.2, 'Total:',weight='semibold',fontsize = 11)
for i in range (0,6):  
       
    ax.text(i+0.2, -0.2, int(som.iloc[i]['sum']),weight='semibold',fontsize = 13)
    
# values = som['sum'].value_counts().keys().tolist()

fig, ax = plt.subplots()

ax=sns.scatterplot(x = 'Income', y= 'Mnt', data = df_treated ,hue= 'cluster' ,palette ='tab10')
plt.xlabel("Income")
plt.ylabel("Total amount spent")
# plt.legend(loc='lower left', bbox_to_anchor=(0.6,0.5))
plt.legend(shadow=True, fancybox=True, ncol = 7, title="Cluster")
plt.ylim(top=4000)
for i in range (0,5):  
    size = df_treated[df_treated['cluster'] == i]['Recency'].size
    percentage = round(size/Total*100,2)  
    
    ax.text(1.1, 0.1+(i-0.9*i), 'Cluster:' + str(i) + '  '+ str(percentage) + ' %  ' + '(N=' + str(size) + ')', transform=ax.transAxes, fontsize=12)

plt.show()

fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(17, 6), dpi=300)
jet = plt.get_cmap('jet')
colors = iter(jet(np.linspace(0,1,5)))

for i in range (0,5):  
    
    size = df_treated[df_treated['cluster'] == i]['Mnt'].size
    percentage = round(size/Total*100,2)
    sns.histplot(df_treated[df_treated['cluster'] == i]['Mnt'],
                 ax = axes[i], bins = 20 ,color=next(colors)).set_title('Cluster ' + str(i+1) + '\n' +'  (N = ' + str(size) +') ' + str(percentage) + '%')
    res = stats (df_treated[df_treated['cluster'] == i]['Mnt'])   
    
    axes[i].text(0.1+i*1.2, -0.15, 'Median = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    
for i in range (1,5):
    
        axes[i].set_ylabel('')  

plt.show()

4.3.2 Key insights

• Cluster (79 %, N = 3799)
  Cluster with the clients that have not purchased recently and do not aprove any campaign

• Cluster (5.36 %, N = 258)
  Cluster with the clients that approved campaign number 4

• Cluster (3.99 %, N = 192)
  Cluster with the clients that approved campaign number 2

• Cluster (4.85 %, N = 233)
  Cluster with the clients that approved campaign number 3

• Cluster (6.8 %, N = 327)
  Cluster with the clients that approved campaign number 1

With only 52 aprovables campaign #5 should not be followed

4.4 Insights of crossing the different segmentations

df_product_vs_campaign = pd.crosstab(df_treated.cluster, df_treated.value_cluster)

fig, ax = plt.subplots()

Costumer_labels = ['Do not accept any campaigns\n(N=3799)', 'Aceppted campaign 4\n(N=258)' , 'Aceppted campaign 2\n(N=192)' , 'Aceppted campaign 3\n(N=233)' , 'Aceppted campaign 1\n(N=327)']
Value_labels = ['Recent casual buyers\n(N=1376) ', 'Non-recent Premium buyers\n(N=1065) ' , 'Recent Premium buyers\n(N=1041) ' , 'Non-recent casual buyers\n(N=1328) ' ]



sns.heatmap(df_product_vs_campaign, linewidths=.5, annot=True, cmap="YlGnBu_r" ,fmt='g')
ax.set_xlabel('Customer value segmentation')
ax.set_ylabel('Customer campaign segmentation')
ax.set_yticklabels(Costumer_labels, rotation = 'horizontal')
ax.set_xticklabels(Value_labels, rotation = 'vertical')




plt.show()

Campanha 1 para casual buyers (pessoas que gastam pouco MNT), Campanha 2,3,4 mais viradas para premium buyers

df_product_vs_value= pd.crosstab(df_treated.value_cluster, df_treated.product_clusters)

fig, ax = plt.subplots()

product_labels = ['Non-electronics\n(N=340)', 'Low-budget customers\n(N=1600)' , 'High value customers\n(N=1727)' , 'Non-stationary/books\n(N=309)' , 'Non-wellness/beauty\n(N=393)', 'Money grubber\n(N=440)']


sns.heatmap(df_product_vs_value, linewidths=.5, annot=True, cmap="YlGnBu" ,fmt='g')
ax.set_xlabel('Product segmentation')
ax.set_ylabel('Customer value segmentation')
ax.set_yticklabels(Value_labels, rotation = 'horizontal')
ax.set_xticklabels(product_labels, rotation = 'vertical')
plt.show()

Vender produtos por catalogo focar no cluster "high value costumers"

To sell eletronic products should focus on clusters : ("high value costumers","non-stationary/books","non-wellness and beauty")

df_1_vs_2 = pd.crosstab(df_treated.cluster, df_treated.product_clusters)

fig, ax = plt.subplots()


sns.heatmap(df_1_vs_2, linewidths=.5, annot=True, cmap="YlGnBu" ,fmt='g')
ax.set_xlabel('Product segmentation')
ax.set_ylabel('Campaign segmentation')
ax.set_yticklabels(Costumer_labels, rotation = 'horizontal')
ax.set_xticklabels(product_labels, rotation = 'vertical')

[Text(0.5, 0, 'Non-electronics\n(N=340)'),
 Text(1.5, 0, 'Low-budget customers\n(N=1600)'),
 Text(2.5, 0, 'High value customers\n(N=1727)'),
 Text(3.5, 0, 'Non-stationary/books\n(N=309)'),
 Text(4.5, 0, 'Non-wellness/beauty\n(N=393)'),
 Text(5.5, 0, 'Money grubber\n(N=440)')]

4.5 Clustering with Gaussian mixture vs Agglomerative Clustering

df_costumer_segmentation_2 = df_normalized.copy()
# df_costumer_segmentation_2.describe().T

df_costumer_segmentation_2= df_costumer_segmentation.filter(items=['NumDealsPurchases',
                 'Mnt','Income']).copy()
df_costumer_segmentation_2.head(n = 5)

	NumDealsPurchases	Mnt	Income
Card_ID
10003075	0.166667	0.470755	0.477529
10003076	0.166667	0.584325	0.597906
10003078	0.500000	0.381957	0.492624
10003079	0.166667	0.507754	0.483320
10003080	0.833333	0.026446	0.107343

from sklearn.mixture import GaussianMixture

# import hierarchical clustering libraries
import scipy.cluster.hierarchy as sch
from sklearn.cluster import AgglomerativeClustering

clustering = AgglomerativeClustering(n_clusters=4).fit(df_costumer_segmentation_2)

df_treated['value_cluster2'] = clustering.labels_
df_costumer_segmentation_2['value_cluster2'] = clustering.labels_
df_costumer_segmentation_2.head(n=5)

	NumDealsPurchases	Mnt	Income	value_cluster2
Card_ID
10003075	0.166667	0.470755	0.477529	3
10003076	0.166667	0.584325	0.597906	3
10003078	0.500000	0.381957	0.492624	1
10003079	0.166667	0.507754	0.483320	3
10003080	0.833333	0.026446	0.107343	0

fig, (ax1, ax2) =plt.subplots(1,2,figsize =(15, 5))

sns.scatterplot(x = 'Income', y= 'Mnt', data = df_costumer_segmentation_2 ,hue = 'value_cluster2', palette = 'viridis',alpha = 0.8, ax=ax1)
ax2.scatter(df_costumer_segmentation_2['Income'], df_costumer_segmentation_2['Mnt'],  
           c = GaussianMixture(n_components = 4,  random_state = 100, covariance_type='full').fit_predict(df_costumer_segmentation_2),s=40, cmap='viridis',alpha= 0.8 )

ax1.set_title('Clustering with Agglomerative Clustering')
ax2.set_title('Clustering with Gaussian mixture')
ax1.set_xlabel('Income')
ax1.set_ylabel('Total amount spent')
ax2.set_xlabel('Income')
ax2.set_ylabel('Total amount spent')
plt.show()

fig, axes = plt.subplots(nrows=1, ncols=4, figsize=(17, 6), dpi=300)
jet = plt.get_cmap('viridis')
colors = iter(jet(np.linspace(0,1,4)))

for i in range (0,4):  
    
    size = df_treated[df_treated['value_cluster2'] == i]['NumDealsPurchases'].size
    percentage = round(size/Total*100,2)
    sns.histplot(df_treated[df_treated['value_cluster2'] == i]['NumDealsPurchases'],
                 ax = axes[i], bins = 20 ,color=next(colors)).set_title('value_cluster ' + str(i+1) + '\n' +'  (N = ' + str(size) +') ' + str(percentage) + '%')
    res = stats2 (df_treated[df_treated['value_cluster2'] == i]['NumDealsPurchases'])   
    
    axes[i].text(0.1+i*1.2, -0.15, 'Mean = ' + str(res[0]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.2, 'Skewness = ' + str(res[3]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.25, 'Q1 = ' + str(res[1]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    axes[i].text(0.1+i*1.2, -0.3, 'Q3 = ' + str(res[2]), transform=axes[0].transAxes, fontsize=10,
           color= 'k')
    
for i in range (1,4):
    
        axes[i].set_ylabel('')  

plt.show()